CM30174 + CM50206 Introduction to Intelligent Agents Marina De Vos, - - PowerPoint PPT Presentation
Applications of Agents Agent characteristics Agent architecture Summary CM30174 + CM50206 Introduction to Intelligent Agents Marina De Vos, Julian Padget Introduction / version 0.4 October 3, 2011 De Vos/Padget (Bath/CS) CM30174/Intro
Applications of Agents Agent characteristics Agent architecture Summary
Marina De Vos, Julian Padget
Introduction / version 0.4
October 3, 2011
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 1 / 35
Applications of Agents Agent characteristics Agent architecture Summary
“An Introduction to Multiagent Systems”, Chapters 1 and 2 [Wooldridge, 2009]. Agentlink material supplied by Mike Luck.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 2 / 35
Applications of Agents Agent characteristics Agent architecture Summary
“An Introduction to Multiagent Systems”, Chapters 1 and 2 [Wooldridge, 2009]. Agentlink material supplied by Mike Luck.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 2 / 35
Applications of Agents Agent characteristics Agent architecture Summary
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Applications of Agents
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Agent characteristics
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Agent architecture
4
Summary
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 3 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agents might help solve some difficult problems BUT they also create new ones:
Independent action ⇒ responsibility, but whose? for what? How to engineer reliable MAS? A new challenge for SE? Software to cooperate, coordinate, negotiate, adapt, argue
Application areas? Here are some examples collected by the Agentlink network
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 4 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agents might help solve some difficult problems BUT they also create new ones:
Independent action ⇒ responsibility, but whose? for what? How to engineer reliable MAS? A new challenge for SE? Software to cooperate, coordinate, negotiate, adapt, argue
Application areas? Here are some examples collected by the Agentlink network
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 4 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agents might help solve some difficult problems BUT they also create new ones:
Independent action ⇒ responsibility, but whose? for what? How to engineer reliable MAS? A new challenge for SE? Software to cooperate, coordinate, negotiate, adapt, argue
Application areas? Here are some examples collected by the Agentlink network
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 4 / 35
Monday, 14 June 2010
www.dcs.kcl.ac.uk/staf/mml
reduction without compromising delivery times
customer order patterns, factory capacity, machine speeds, order batching and warehouse size, etc.)
customers, by determining the necessary plant capacity and the incurred costs of serving these customers
delivery commitments
4
Monday, 14 June 2010
Monday, 14 June 2010
www.dcs.kcl.ac.uk/staf/mml
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Monday, 14 June 2010
Monday, 14 June 2010
www.dcs.kcl.ac.uk/staf/mml
customer
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Monday, 14 June 2010
Monday, 14 June 2010
www.dcs.kcl.ac.uk/staf/mml
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Monday, 14 June 2010
Monday, 14 June 2010
www.dcs.kcl.ac.uk/staf/mml
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Monday, 14 June 2010
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
1
Applications of Agents
2
Agent characteristics Are agents new or different? Agents and their environment The intentional perspective
3
Agent architecture
4
Summary
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 5 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
An intelligent agent is a computer system capable of flexible, autonomous action in some environment: AGENT ENVIRONMENT act sense The situated agent.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 6 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
An agent can be more useful in the context of others:
Can concentrate on tasks within competence Can delegate other tasks Can use ability to communicate, coordinate, negotiate How to organize?
act sense act sense act sense
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 7 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
An agent can be more useful in the context of others:
Can concentrate on tasks within competence Can delegate other tasks Can use ability to communicate, coordinate, negotiate How to organize?
act sense act sense act sense
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 7 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
An agent can be more useful in the context of others:
Can concentrate on tasks within competence Can delegate other tasks Can use ability to communicate, coordinate, negotiate How to organize?
act sense act sense act sense
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 7 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
An agent can be more useful in the context of others:
Can concentrate on tasks within competence Can delegate other tasks Can use ability to communicate, coordinate, negotiate How to organize?
act sense act sense act sense
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 7 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
An agent can be more useful in the context of others:
Can concentrate on tasks within competence Can delegate other tasks Can use ability to communicate, coordinate, negotiate How to organize?
act sense act sense act sense
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 7 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Major: Reactive: has an on-going interaction with its environment, and responds to changes that occur in it (in time for the response to be useful). Pro-active: means generating and attempting to achieve goals Social: ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 8 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Major: Reactive: has an on-going interaction with its environment, and responds to changes that occur in it (in time for the response to be useful). Pro-active: means generating and attempting to achieve goals Social: ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 8 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Major: Reactive: has an on-going interaction with its environment, and responds to changes that occur in it (in time for the response to be useful). Pro-active: means generating and attempting to achieve goals Social: ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 8 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Minor: Mobility: The ability of an agent to move around an electronic network. Veracity: Whether an agent will knowingly communicate false information. Benevolence: Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality: Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption: Whether agents improve performance
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Minor: Mobility: The ability of an agent to move around an electronic network. Veracity: Whether an agent will knowingly communicate false information. Benevolence: Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality: Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption: Whether agents improve performance
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Minor: Mobility: The ability of an agent to move around an electronic network. Veracity: Whether an agent will knowingly communicate false information. Benevolence: Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality: Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption: Whether agents improve performance
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Minor: Mobility: The ability of an agent to move around an electronic network. Veracity: Whether an agent will knowingly communicate false information. Benevolence: Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality: Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption: Whether agents improve performance
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Minor: Mobility: The ability of an agent to move around an electronic network. Veracity: Whether an agent will knowingly communicate false information. Benevolence: Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality: Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption: Whether agents improve performance
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Simple and not-so-simple agents:
thermostat washing machines engine management systems? house management system — “intelligent buildings”?
If environment never changes, success or failure are meaningless — program executes blindly The real world is not like that: change, incompleteness. Many (most?) interesting environments are dynamic Software is hard to build: planning, failures, choice A reactive system interacts continuously with environment, responds to changes – consider a robot sharing an environment with people...
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 10 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Simple and not-so-simple agents:
thermostat washing machines engine management systems? house management system — “intelligent buildings”?
If environment never changes, success or failure are meaningless — program executes blindly The real world is not like that: change, incompleteness. Many (most?) interesting environments are dynamic Software is hard to build: planning, failures, choice A reactive system interacts continuously with environment, responds to changes – consider a robot sharing an environment with people...
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 10 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Simple and not-so-simple agents:
thermostat washing machines engine management systems? house management system — “intelligent buildings”?
If environment never changes, success or failure are meaningless — program executes blindly The real world is not like that: change, incompleteness. Many (most?) interesting environments are dynamic Software is hard to build: planning, failures, choice A reactive system interacts continuously with environment, responds to changes – consider a robot sharing an environment with people...
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 10 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Simple and not-so-simple agents:
thermostat washing machines engine management systems? house management system — “intelligent buildings”?
If environment never changes, success or failure are meaningless — program executes blindly The real world is not like that: change, incompleteness. Many (most?) interesting environments are dynamic Software is hard to build: planning, failures, choice A reactive system interacts continuously with environment, responds to changes – consider a robot sharing an environment with people...
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 10 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Simple and not-so-simple agents:
thermostat washing machines engine management systems? house management system — “intelligent buildings”?
If environment never changes, success or failure are meaningless — program executes blindly The real world is not like that: change, incompleteness. Many (most?) interesting environments are dynamic Software is hard to build: planning, failures, choice A reactive system interacts continuously with environment, responds to changes – consider a robot sharing an environment with people...
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 10 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Reactive systems are relatively easy: stimulus → response Want agents to do things for us Want goal-directed behaviour — implies AI techniques, e.g. reasoning with rules Pro-activity: generating and achieving goals
Not driven (solely) by events Taking the initiative Recognising opportunities
Need a model of the environment to support the decision-making process:
symbolic — classical AI non-symbolic — neural networks, time series, Markov decision processes etc.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 11 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Reactive systems are relatively easy: stimulus → response Want agents to do things for us Want goal-directed behaviour — implies AI techniques, e.g. reasoning with rules Pro-activity: generating and achieving goals
Not driven (solely) by events Taking the initiative Recognising opportunities
Need a model of the environment to support the decision-making process:
symbolic — classical AI non-symbolic — neural networks, time series, Markov decision processes etc.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 11 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Reactive systems are relatively easy: stimulus → response Want agents to do things for us Want goal-directed behaviour — implies AI techniques, e.g. reasoning with rules Pro-activity: generating and achieving goals
Not driven (solely) by events Taking the initiative Recognising opportunities
Need a model of the environment to support the decision-making process:
symbolic — classical AI non-symbolic — neural networks, time series, Markov decision processes etc.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 11 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Reactive systems are relatively easy: stimulus → response Want agents to do things for us Want goal-directed behaviour — implies AI techniques, e.g. reasoning with rules Pro-activity: generating and achieving goals
Not driven (solely) by events Taking the initiative Recognising opportunities
Need a model of the environment to support the decision-making process:
symbolic — classical AI non-symbolic — neural networks, time series, Markov decision processes etc.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 11 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Reactive systems are relatively easy: stimulus → response Want agents to do things for us Want goal-directed behaviour — implies AI techniques, e.g. reasoning with rules Pro-activity: generating and achieving goals
Not driven (solely) by events Taking the initiative Recognising opportunities
Need a model of the environment to support the decision-making process:
symbolic — classical AI non-symbolic — neural networks, time series, Markov decision processes etc.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 11 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The real world is a multi-agent environment: we cannot go around attempting to achieve goals without taking others into account. Some goals can only be achieved with the cooperation of
Information/models of other agents’ state Trust metrics Reputation models (e.g. FOAF)
Social ability in agents is the ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 12 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The real world is a multi-agent environment: we cannot go around attempting to achieve goals without taking others into account. Some goals can only be achieved with the cooperation of
Information/models of other agents’ state Trust metrics Reputation models (e.g. FOAF)
Social ability in agents is the ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 12 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The real world is a multi-agent environment: we cannot go around attempting to achieve goals without taking others into account. Some goals can only be achieved with the cooperation of
Information/models of other agents’ state Trust metrics Reputation models (e.g. FOAF)
Social ability in agents is the ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 12 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Object:
Encapsulates some state Communicates via message passing Has methods — operations on object state Objects do what they are told
Agents:
Autonomous: decision procedure inside the agent determines whether or not to perform an action on request from another agent Smart: capable of flexible (reactive, pro-active, social) behaviour Active: a multi-agent system is multi-threaded Agents do something because they want to (benevolence) Agents do something for gain (utility)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 13 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Object:
Encapsulates some state Communicates via message passing Has methods — operations on object state Objects do what they are told
Agents:
Autonomous: decision procedure inside the agent determines whether or not to perform an action on request from another agent Smart: capable of flexible (reactive, pro-active, social) behaviour Active: a multi-agent system is multi-threaded Agents do something because they want to (benevolence) Agents do something for gain (utility)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 13 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Expert systems typically disembodied ‘expertise’ about some (abstract) domain of discourse. Example: MYCIN knows about blood diseases in humans. Knowledge is stored as rules. A doctor enters facts, answers questions and puts questions to obtain advice. Main differences:
Agents are situated in an environment: MYCIN is not aware
Only information obtained is from asking the user questions Agents act: MYCIN does not operate on patients.
Some real-time (typically process control) expert systems are agents. Expert systems are useful components of agents
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 14 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Expert systems typically disembodied ‘expertise’ about some (abstract) domain of discourse. Example: MYCIN knows about blood diseases in humans. Knowledge is stored as rules. A doctor enters facts, answers questions and puts questions to obtain advice. Main differences:
Agents are situated in an environment: MYCIN is not aware
Only information obtained is from asking the user questions Agents act: MYCIN does not operate on patients.
Some real-time (typically process control) expert systems are agents. Expert systems are useful components of agents
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 14 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Expert systems typically disembodied ‘expertise’ about some (abstract) domain of discourse. Example: MYCIN knows about blood diseases in humans. Knowledge is stored as rules. A doctor enters facts, answers questions and puts questions to obtain advice. Main differences:
Agents are situated in an environment: MYCIN is not aware
Only information obtained is from asking the user questions Agents act: MYCIN does not operate on patients.
Some real-time (typically process control) expert systems are agents. Expert systems are useful components of agents
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 14 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Expert systems typically disembodied ‘expertise’ about some (abstract) domain of discourse. Example: MYCIN knows about blood diseases in humans. Knowledge is stored as rules. A doctor enters facts, answers questions and puts questions to obtain advice. Main differences:
Agents are situated in an environment: MYCIN is not aware
Only information obtained is from asking the user questions Agents act: MYCIN does not operate on patients.
Some real-time (typically process control) expert systems are agents. Expert systems are useful components of agents
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 14 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Expert systems typically disembodied ‘expertise’ about some (abstract) domain of discourse. Example: MYCIN knows about blood diseases in humans. Knowledge is stored as rules. A doctor enters facts, answers questions and puts questions to obtain advice. Main differences:
Agents are situated in an environment: MYCIN is not aware
Only information obtained is from asking the user questions Agents act: MYCIN does not operate on patients.
Some real-time (typically process control) expert systems are agents. Expert systems are useful components of agents
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 14 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Accessible vs. inaccessible: where the agent can obtain complete, accurate, up-to-date information about the state
environments are inaccessible. Accessibility ⇒ simpler to build. Deterministic vs. non-deterministic: where any action has a single guaranteed effect — there is no uncertainty about the state resulting from an action. The physical world is (largely!) non-deterministic. Non-determinism ⇒ more problems. Static vs. dynamic: where only the agent’s action changes the environment. Other processes — outside the agent’s control — operate in a dynamic environment — just like the physical world.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 15 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Accessible vs. inaccessible: where the agent can obtain complete, accurate, up-to-date information about the state
environments are inaccessible. Accessibility ⇒ simpler to build. Deterministic vs. non-deterministic: where any action has a single guaranteed effect — there is no uncertainty about the state resulting from an action. The physical world is (largely!) non-deterministic. Non-determinism ⇒ more problems. Static vs. dynamic: where only the agent’s action changes the environment. Other processes — outside the agent’s control — operate in a dynamic environment — just like the physical world.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 15 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Accessible vs. inaccessible: where the agent can obtain complete, accurate, up-to-date information about the state
environments are inaccessible. Accessibility ⇒ simpler to build. Deterministic vs. non-deterministic: where any action has a single guaranteed effect — there is no uncertainty about the state resulting from an action. The physical world is (largely!) non-deterministic. Non-determinism ⇒ more problems. Static vs. dynamic: where only the agent’s action changes the environment. Other processes — outside the agent’s control — operate in a dynamic environment — just like the physical world.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 15 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Episodic vs. non-episodic: where the agent performance depends on several discrete episodes, but each episode is
chooses an action based only on the current episode — there is no need to consider either the past or the future. Discrete vs. continuous: where there are a fixed, finite number of actions and percepts. For example a chess game is a discrete environment, while driving a taxi is a continuous environment.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 16 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Episodic vs. non-episodic: where the agent performance depends on several discrete episodes, but each episode is
chooses an action based only on the current episode — there is no need to consider either the past or the future. Discrete vs. continuous: where there are a fixed, finite number of actions and percepts. For example a chess game is a discrete environment, while driving a taxi is a continuous environment.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 16 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The philosopher Daniel Dennett coined the term intentional system to describe entities ‘whose behaviour can be predicted by the method of attributing belief, desires and rational acumen’. Dennett identifies different ‘grades’ of intentional system:
‘A first order intentional system has beliefs and desires (etc.) but no beliefs and desires about beliefs and desires... A second order intentional system is more sophisticated; it has beliefs and desires (and no doubt other intentional states) about beliefs and desires (and other intentional states) — both those of others and its own’
Basis for the Belief-Desire-Intention (BDI) model of agency
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 17 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The philosopher Daniel Dennett coined the term intentional system to describe entities ‘whose behaviour can be predicted by the method of attributing belief, desires and rational acumen’. Dennett identifies different ‘grades’ of intentional system:
‘A first order intentional system has beliefs and desires (etc.) but no beliefs and desires about beliefs and desires... A second order intentional system is more sophisticated; it has beliefs and desires (and no doubt other intentional states) about beliefs and desires (and other intentional states) — both those of others and its own’
Basis for the Belief-Desire-Intention (BDI) model of agency
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 17 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The philosopher Daniel Dennett coined the term intentional system to describe entities ‘whose behaviour can be predicted by the method of attributing belief, desires and rational acumen’. Dennett identifies different ‘grades’ of intentional system:
‘A first order intentional system has beliefs and desires (etc.) but no beliefs and desires about beliefs and desires... A second order intentional system is more sophisticated; it has beliefs and desires (and no doubt other intentional states) about beliefs and desires (and other intentional states) — both those of others and its own’
Basis for the Belief-Desire-Intention (BDI) model of agency
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 17 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The philosopher Daniel Dennett coined the term intentional system to describe entities ‘whose behaviour can be predicted by the method of attributing belief, desires and rational acumen’. Dennett identifies different ‘grades’ of intentional system:
‘A first order intentional system has beliefs and desires (etc.) but no beliefs and desires about beliefs and desires... A second order intentional system is more sophisticated; it has beliefs and desires (and no doubt other intentional states) about beliefs and desires (and other intentional states) — both those of others and its own’
Basis for the Belief-Desire-Intention (BDI) model of agency
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 17 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
The philosopher Daniel Dennett coined the term intentional system to describe entities ‘whose behaviour can be predicted by the method of attributing belief, desires and rational acumen’. Dennett identifies different ‘grades’ of intentional system:
‘A first order intentional system has beliefs and desires (etc.) but no beliefs and desires about beliefs and desires... A second order intentional system is more sophisticated; it has beliefs and desires (and no doubt other intentional states) about beliefs and desires (and other intentional states) — both those of others and its own’
Basis for the Belief-Desire-Intention (BDI) model of agency
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 17 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
As computer systems become more complex, we need more powerful abstractions and metaphors to explain their
The most important phases in computing are identified by new abstractions:
procedural abstraction abstract data types
next: agents? or services? both! http://www.ist-alive.eu
Abstractions help in solving problems because they replace lots of detail with a single concept Agents, and agents as intentional systems, represent an abstraction both for software components and software systems
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 18 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
As computer systems become more complex, we need more powerful abstractions and metaphors to explain their
The most important phases in computing are identified by new abstractions:
procedural abstraction abstract data types
next: agents? or services? both! http://www.ist-alive.eu
Abstractions help in solving problems because they replace lots of detail with a single concept Agents, and agents as intentional systems, represent an abstraction both for software components and software systems
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 18 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
As computer systems become more complex, we need more powerful abstractions and metaphors to explain their
The most important phases in computing are identified by new abstractions:
procedural abstraction abstract data types
next: agents? or services? both! http://www.ist-alive.eu
Abstractions help in solving problems because they replace lots of detail with a single concept Agents, and agents as intentional systems, represent an abstraction both for software components and software systems
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 18 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
As computer systems become more complex, we need more powerful abstractions and metaphors to explain their
The most important phases in computing are identified by new abstractions:
procedural abstraction abstract data types
next: agents? or services? both! http://www.ist-alive.eu
Abstractions help in solving problems because they replace lots of detail with a single concept Agents, and agents as intentional systems, represent an abstraction both for software components and software systems
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 18 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include
Beyond declarative programming:
Procedural programming states how a system operates — too fragile Declarative programming states what to achieve, declares relationships between objects, lets a built-in control mechanism solve the problem — more robust Agent programming states what to achieve, relies on agent control mechanisms solve problem, respects some built-in theory of agency (e.g. Cohen-Levesque model of intention)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 19 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include
Beyond declarative programming:
Procedural programming states how a system operates — too fragile Declarative programming states what to achieve, declares relationships between objects, lets a built-in control mechanism solve the problem — more robust Agent programming states what to achieve, relies on agent control mechanisms solve problem, respects some built-in theory of agency (e.g. Cohen-Levesque model of intention)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 19 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include
Beyond declarative programming:
Procedural programming states how a system operates — too fragile Declarative programming states what to achieve, declares relationships between objects, lets a built-in control mechanism solve the problem — more robust Agent programming states what to achieve, relies on agent control mechanisms solve problem, respects some built-in theory of agency (e.g. Cohen-Levesque model of intention)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 19 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include
Beyond declarative programming:
Procedural programming states how a system operates — too fragile Declarative programming states what to achieve, declares relationships between objects, lets a built-in control mechanism solve the problem — more robust Agent programming states what to achieve, relies on agent control mechanisms solve problem, respects some built-in theory of agency (e.g. Cohen-Levesque model of intention)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 19 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include
Beyond declarative programming:
Procedural programming states how a system operates — too fragile Declarative programming states what to achieve, declares relationships between objects, lets a built-in control mechanism solve the problem — more robust Agent programming states what to achieve, relies on agent control mechanisms solve problem, respects some built-in theory of agency (e.g. Cohen-Levesque model of intention)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 19 / 35
Applications of Agents Agent characteristics Agent architecture Summary Are agents new or different? Agents and their environment The intentional perspective
Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include
Beyond declarative programming:
Procedural programming states how a system operates — too fragile Declarative programming states what to achieve, declares relationships between objects, lets a built-in control mechanism solve the problem — more robust Agent programming states what to achieve, relies on agent control mechanisms solve problem, respects some built-in theory of agency (e.g. Cohen-Levesque model of intention)
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 19 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
1
Applications of Agents
2
Agent characteristics
3
Agent architecture Models of architecture and environment Kinds of agents Goals and actions
4
Summary
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 20 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Environment is a finite set E of discrete, instantaneous states: E = {e0, e1, . . .} Agents actions change the environment. Ac = {α0, α1, . . .} An agent acting in an environment generates a run, r: r : e0
α0
− → e1
α1
− → e2
α2
− → e3 . . . eu An agent senses the environment state ei ∈ E and takes action αi ∈ Ac Exercise: E = {light, dark}, Ac = {on, off} What are the runs?
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 21 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Environment is a finite set E of discrete, instantaneous states: E = {e0, e1, . . .} Agents actions change the environment. Ac = {α0, α1, . . .} An agent acting in an environment generates a run, r: r : e0
α0
− → e1
α1
− → e2
α2
− → e3 . . . eu An agent senses the environment state ei ∈ E and takes action αi ∈ Ac Exercise: E = {light, dark}, Ac = {on, off} What are the runs?
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 21 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Environment is a finite set E of discrete, instantaneous states: E = {e0, e1, . . .} Agents actions change the environment. Ac = {α0, α1, . . .} An agent acting in an environment generates a run, r: r : e0
α0
− → e1
α1
− → e2
α2
− → e3 . . . eu An agent senses the environment state ei ∈ E and takes action αi ∈ Ac Exercise: E = {light, dark}, Ac = {on, off} What are the runs?
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 21 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Environment is a finite set E of discrete, instantaneous states: E = {e0, e1, . . .} Agents actions change the environment. Ac = {α0, α1, . . .} An agent acting in an environment generates a run, r: r : e0
α0
− → e1
α1
− → e2
α2
− → e3 . . . eu An agent senses the environment state ei ∈ E and takes action αi ∈ Ac Exercise: E = {light, dark}, Ac = {on, off} What are the runs?
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 21 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Environment is a finite set E of discrete, instantaneous states: E = {e0, e1, . . .} Agents actions change the environment. Ac = {α0, α1, . . .} An agent acting in an environment generates a run, r: r : e0
α0
− → e1
α1
− → e2
α2
− → e3 . . . eu An agent senses the environment state ei ∈ E and takes action αi ∈ Ac Exercise: E = {light, dark}, Ac = {on, off} What are the runs?
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 21 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Define R as the set of all runs then RAc ⊂ R that ends with an action, and RE ⊂ R that ends with an environment state A state transformer function τ : RAc → 2E Environments are: history dependent and non-deterministic If τ(r) = ∅, there are no possible successor states to r: the run has ended An environment Env is a triple Env = E, e0, τ where E is set of environment states, e0 ∈ E is the initial state; and τ is the state transformer function.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 22 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
An Agent is a function that maps runs to actions: Ag : RE → Ac Thus an agent makes a decision about what action to perform based on the history of the system witnessed to date. Let AG be the set of all agents.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 23 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
An Agent is a function that maps runs to actions: Ag : RE → Ac Thus an agent makes a decision about what action to perform based on the history of the system witnessed to date. Let AG be the set of all agents.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 23 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
An Agent is a function that maps runs to actions: Ag : RE → Ac Thus an agent makes a decision about what action to perform based on the history of the system witnessed to date. Let AG be the set of all agents.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 23 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
A system is a pair of an agent and an environment. A system induces a set of possible runs: R(Ag, Env). Assume R(Ag, Env) contains only runs that have ended.
that is, ∀r ∈ R : τ(r) = ∅
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 24 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
A system is a pair of an agent and an environment. A system induces a set of possible runs: R(Ag, Env). Assume R(Ag, Env) contains only runs that have ended.
that is, ∀r ∈ R : τ(r) = ∅
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 24 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
A system is a pair of an agent and an environment. A system induces a set of possible runs: R(Ag, Env). Assume R(Ag, Env) contains only runs that have ended.
that is, ∀r ∈ R : τ(r) = ∅
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 24 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Thus, a sequence (e0, α0, e1, α1, e2, . . .) represents a run of an agent Ag in environment Env = E, e0, τ if
e0 is the initial state of Env α0 = Ag(e0) and for u > 0, eu ∈ τ((e0, α0, . . . , αu−1)) and αu = Ag((e0, α0, . . . , eu))
That is, E is finally in state eu as a result of action αu
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Thus, a sequence (e0, α0, e1, α1, e2, . . .) represents a run of an agent Ag in environment Env = E, e0, τ if
e0 is the initial state of Env α0 = Ag(e0) and for u > 0, eu ∈ τ((e0, α0, . . . , αu−1)) and αu = Ag((e0, α0, . . . , eu))
That is, E is finally in state eu as a result of action αu
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Thus, a sequence (e0, α0, e1, α1, e2, . . .) represents a run of an agent Ag in environment Env = E, e0, τ if
e0 is the initial state of Env α0 = Ag(e0) and for u > 0, eu ∈ τ((e0, α0, . . . , αu−1)) and αu = Ag((e0, α0, . . . , eu))
That is, E is finally in state eu as a result of action αu
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Thus, a sequence (e0, α0, e1, α1, e2, . . .) represents a run of an agent Ag in environment Env = E, e0, τ if
e0 is the initial state of Env α0 = Ag(e0) and for u > 0, eu ∈ τ((e0, α0, . . . , αu−1)) and αu = Ag((e0, α0, . . . , eu))
That is, E is finally in state eu as a result of action αu
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Thus, a sequence (e0, α0, e1, α1, e2, . . .) represents a run of an agent Ag in environment Env = E, e0, τ if
e0 is the initial state of Env α0 = Ag(e0) and for u > 0, eu ∈ τ((e0, α0, . . . , αu−1)) and αu = Ag((e0, α0, . . . , eu))
That is, E is finally in state eu as a result of action αu
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Simple but useful Some agents decide what to do without reference to their history We call such agents purely reactive action : E → Ac A thermostat is a purely reactive agent. action(e) =
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Simple but useful Some agents decide what to do without reference to their history We call such agents purely reactive action : E → Ac A thermostat is a purely reactive agent. action(e) =
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Simple but useful Some agents decide what to do without reference to their history We call such agents purely reactive action : E → Ac A thermostat is a purely reactive agent. action(e) =
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Simple but useful Some agents decide what to do without reference to their history We call such agents purely reactive action : E → Ac A thermostat is a purely reactive agent. action(e) =
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Add perception: AGENT see action ENVIRONMENT act sense The see function is the agent’s ability to observe its environment, and the action function represents the agent’s decision making process. Output of the see function is a percept:
see : E → Per maps environment states to percepts action : Per ∗ → Ac maps sequences of percepts to actions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 27 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Add perception: AGENT see action ENVIRONMENT act sense The see function is the agent’s ability to observe its environment, and the action function represents the agent’s decision making process. Output of the see function is a percept:
see : E → Per maps environment states to percepts action : Per ∗ → Ac maps sequences of percepts to actions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 27 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Add perception: AGENT see action ENVIRONMENT act sense The see function is the agent’s ability to observe its environment, and the action function represents the agent’s decision making process. Output of the see function is a percept:
see : E → Per maps environment states to percepts action : Per ∗ → Ac maps sequences of percepts to actions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 27 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Add memory: AGENT see action next state ENVIRONMENT act sense Let I be the set of all internal states of the agent — the agent remembers the states of the environment.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 28 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Add memory: AGENT see action next state ENVIRONMENT act sense Let I be the set of all internal states of the agent — the agent remembers the states of the environment.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 28 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The perception function is unchanged. Redefine action and introduce next
see : E → Per maps environment states to percepts action : I → Ac maps from internal states to actions next : I × Per → I maps an internal state and a percept to an internal state
Agent control loop
1
Agent starts in some initial internal state i0.
2
Observes environment state e, generates a percept see(e).
3
Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
4
The action selected by the agent is action(next(i0, see(e))). This action is then performed.
5
Goto step 2
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 29 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
We build agents in order to carry out tasks for us. The task must be specified by us... But we want to tell agents what to do without telling them how to do it. Why? Because we would get it wrong. Exercise: program a robot to go from A to B Approach:
Actions are solutions to atomic problems Utility functions measure achievment of goal
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 30 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
We build agents in order to carry out tasks for us. The task must be specified by us... But we want to tell agents what to do without telling them how to do it. Why? Because we would get it wrong. Exercise: program a robot to go from A to B Approach:
Actions are solutions to atomic problems Utility functions measure achievment of goal
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 30 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
We build agents in order to carry out tasks for us. The task must be specified by us... But we want to tell agents what to do without telling them how to do it. Why? Because we would get it wrong. Exercise: program a robot to go from A to B Approach:
Actions are solutions to atomic problems Utility functions measure achievment of goal
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 30 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
We build agents in order to carry out tasks for us. The task must be specified by us... But we want to tell agents what to do without telling them how to do it. Why? Because we would get it wrong. Exercise: program a robot to go from A to B Approach:
Actions are solutions to atomic problems Utility functions measure achievment of goal
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 30 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
We build agents in order to carry out tasks for us. The task must be specified by us... But we want to tell agents what to do without telling them how to do it. Why? Because we would get it wrong. Exercise: program a robot to go from A to B Approach:
Actions are solutions to atomic problems Utility functions measure achievment of goal
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 30 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
We build agents in order to carry out tasks for us. The task must be specified by us... But we want to tell agents what to do without telling them how to do it. Why? Because we would get it wrong. Exercise: program a robot to go from A to B Approach:
Actions are solutions to atomic problems Utility functions measure achievment of goal
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 30 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The Tileworld example (Wooldridge, p.39) illustrates the use of utility functions:
Simulated two dimensional grid environment on which there are agents, tiles, obstacles, and holes. An agent can move in four directions, up, down, left, or right, and if it is located next to a tile, it can push it. Holes have to be filled up with tiles by the agent. An agent scores points by filling holes with tiles, with the aim of filling as many holes as possible. Tileworld changes: holes appear and disappear randomly
Utility function can be defined as: u(r) = number of holes filled in r number of holes that appeared in r
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 31 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The Tileworld example (Wooldridge, p.39) illustrates the use of utility functions:
Simulated two dimensional grid environment on which there are agents, tiles, obstacles, and holes. An agent can move in four directions, up, down, left, or right, and if it is located next to a tile, it can push it. Holes have to be filled up with tiles by the agent. An agent scores points by filling holes with tiles, with the aim of filling as many holes as possible. Tileworld changes: holes appear and disappear randomly
Utility function can be defined as: u(r) = number of holes filled in r number of holes that appeared in r
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 31 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The Tileworld example (Wooldridge, p.39) illustrates the use of utility functions:
Simulated two dimensional grid environment on which there are agents, tiles, obstacles, and holes. An agent can move in four directions, up, down, left, or right, and if it is located next to a tile, it can push it. Holes have to be filled up with tiles by the agent. An agent scores points by filling holes with tiles, with the aim of filling as many holes as possible. Tileworld changes: holes appear and disappear randomly
Utility function can be defined as: u(r) = number of holes filled in r number of holes that appeared in r
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 31 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The Tileworld example (Wooldridge, p.39) illustrates the use of utility functions:
Simulated two dimensional grid environment on which there are agents, tiles, obstacles, and holes. An agent can move in four directions, up, down, left, or right, and if it is located next to a tile, it can push it. Holes have to be filled up with tiles by the agent. An agent scores points by filling holes with tiles, with the aim of filling as many holes as possible. Tileworld changes: holes appear and disappear randomly
Utility function can be defined as: u(r) = number of holes filled in r number of holes that appeared in r
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 31 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The Tileworld example (Wooldridge, p.39) illustrates the use of utility functions:
Simulated two dimensional grid environment on which there are agents, tiles, obstacles, and holes. An agent can move in four directions, up, down, left, or right, and if it is located next to a tile, it can push it. Holes have to be filled up with tiles by the agent. An agent scores points by filling holes with tiles, with the aim of filling as many holes as possible. Tileworld changes: holes appear and disappear randomly
Utility function can be defined as: u(r) = number of holes filled in r number of holes that appeared in r
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 31 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
The Tileworld example (Wooldridge, p.39) illustrates the use of utility functions:
Simulated two dimensional grid environment on which there are agents, tiles, obstacles, and holes. An agent can move in four directions, up, down, left, or right, and if it is located next to a tile, it can push it. Holes have to be filled up with tiles by the agent. An agent scores points by filling holes with tiles, with the aim of filling as many holes as possible. Tileworld changes: holes appear and disappear randomly
Utility function can be defined as: u(r) = number of holes filled in r number of holes that appeared in r
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 31 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Two most common types of tasks are achievement tasks and maintenance tasks An achievement task is a set G of “good” or “goal” states: G ⊂ E
The agent succeeds if it is guaranteed to bring about at least one of these states There is no preference ordering on the states
A maintenance goal is a set B of “bad” states: B ⊂ E
The agent succeeds in a particular environment if it manages to avoid all states in B That is, it never performs actions which result in any state in B occurring.
Can express these goals in terms of utility functions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 32 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Two most common types of tasks are achievement tasks and maintenance tasks An achievement task is a set G of “good” or “goal” states: G ⊂ E
The agent succeeds if it is guaranteed to bring about at least one of these states There is no preference ordering on the states
A maintenance goal is a set B of “bad” states: B ⊂ E
The agent succeeds in a particular environment if it manages to avoid all states in B That is, it never performs actions which result in any state in B occurring.
Can express these goals in terms of utility functions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 32 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Two most common types of tasks are achievement tasks and maintenance tasks An achievement task is a set G of “good” or “goal” states: G ⊂ E
The agent succeeds if it is guaranteed to bring about at least one of these states There is no preference ordering on the states
A maintenance goal is a set B of “bad” states: B ⊂ E
The agent succeeds in a particular environment if it manages to avoid all states in B That is, it never performs actions which result in any state in B occurring.
Can express these goals in terms of utility functions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 32 / 35
Applications of Agents Agent characteristics Agent architecture Summary Models of architecture and environment Kinds of agents Goals and actions
Two most common types of tasks are achievement tasks and maintenance tasks An achievement task is a set G of “good” or “goal” states: G ⊂ E
The agent succeeds if it is guaranteed to bring about at least one of these states There is no preference ordering on the states
A maintenance goal is a set B of “bad” states: B ⊂ E
The agent succeeds in a particular environment if it manages to avoid all states in B That is, it never performs actions which result in any state in B occurring.
Can express these goals in terms of utility functions
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 32 / 35
Applications of Agents Agent characteristics Agent architecture Summary
1
Applications of Agents
2
Agent characteristics
3
Agent architecture
4
Summary
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 33 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agent characteristics Is an agent something new? Intentional perspective on software systems Abstract agent architecture: agent + environments Kinds of agents: reactive, perceptive, stateful Utility functions: one way to say what, not how
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 34 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agent characteristics Is an agent something new? Intentional perspective on software systems Abstract agent architecture: agent + environments Kinds of agents: reactive, perceptive, stateful Utility functions: one way to say what, not how
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 34 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agent characteristics Is an agent something new? Intentional perspective on software systems Abstract agent architecture: agent + environments Kinds of agents: reactive, perceptive, stateful Utility functions: one way to say what, not how
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 34 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agent characteristics Is an agent something new? Intentional perspective on software systems Abstract agent architecture: agent + environments Kinds of agents: reactive, perceptive, stateful Utility functions: one way to say what, not how
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 34 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agent characteristics Is an agent something new? Intentional perspective on software systems Abstract agent architecture: agent + environments Kinds of agents: reactive, perceptive, stateful Utility functions: one way to say what, not how
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 34 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Agent characteristics Is an agent something new? Intentional perspective on software systems Abstract agent architecture: agent + environments Kinds of agents: reactive, perceptive, stateful Utility functions: one way to say what, not how
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 34 / 35
Applications of Agents Agent characteristics Agent architecture Summary
Wooldridge Chs.1-2. Russell and Norvig Ch.1. Russell, S. and Norvig, P . (2003). Artificial Intelligence: A Modern Approach. Prentice Hall. ISBN: 0-13-080302-2. Wooldridge, M. (2009). An introduction to multiagent systems (second edition). Wiley. ISBN: 978-0-470-51946-2.
De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 35 / 35