Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 over time. De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 over time. De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 over time. De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 over time. De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics 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 over time. De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 9 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Reactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Reactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Reactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Reactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Reactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Proactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Proactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Proactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Proactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Proactivity 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Social Ability 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 others. Suggests need for: 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Social Ability 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 others. Suggests need for: 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Social Ability 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 others. Suggests need for: 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Objects 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Objects 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Expert Systems 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 of the world 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Expert Systems 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 of the world 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Expert Systems 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 of the world 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Expert Systems 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 of the world 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents are not Expert Systems 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 of the world 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Environment Characteristics 1/2 Accessible vs. inaccessible: where the agent can obtain complete, accurate, up-to-date information about the state of the environment. Most moderately complex 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Environment Characteristics 1/2 Accessible vs. inaccessible: where the agent can obtain complete, accurate, up-to-date information about the state of the environment. Most moderately complex 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Environment Characteristics 1/2 Accessible vs. inaccessible: where the agent can obtain complete, accurate, up-to-date information about the state of the environment. Most moderately complex 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Environment Characteristics 2/2 Episodic vs. non-episodic: where the agent performance depends on several discrete episodes, but each episode is independent. Simplifies development because the agent 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Environment Characteristics 2/2 Episodic vs. non-episodic: where the agent performance depends on several discrete episodes, but each episode is independent. Simplifies development because the agent 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 1/3 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 1/3 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 1/3 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 1/3 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 1/3 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 2/3 As computer systems become more complex, we need more powerful abstractions and metaphors to explain their operation — low level explanations become impractical. The most important phases in computing are identified by new abstractions: procedural abstraction abstract data types objects 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 2/3 As computer systems become more complex, we need more powerful abstractions and metaphors to explain their operation — low level explanations become impractical. The most important phases in computing are identified by new abstractions: procedural abstraction abstract data types objects 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 2/3 As computer systems become more complex, we need more powerful abstractions and metaphors to explain their operation — low level explanations become impractical. The most important phases in computing are identified by new abstractions: procedural abstraction abstract data types objects 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 2/3 As computer systems become more complex, we need more powerful abstractions and metaphors to explain their operation — low level explanations become impractical. The most important phases in computing are identified by new abstractions: procedural abstraction abstract data types objects 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 3/3 Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include other systems. Legacy systems can be embedded 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 3/3 Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include other systems. Legacy systems can be embedded 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 3/3 Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include other systems. Legacy systems can be embedded 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 3/3 Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include other systems. Legacy systems can be embedded 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 3/3 Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include other systems. Legacy systems can be embedded 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 Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agents as Intentional Systems 3/3 Characterising Agents: Provides familiar, non-technical way to understand and explain agents. Good for requirements. Nested Representations: Can specify systems that include other systems. Legacy systems can be embedded 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Content Applications of Agents 1 Agent characteristics 2 Agent architecture 3 Models of architecture and environment Kinds of agents Goals and actions Summary 4 De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 20 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary An Abstract Architecture for Agents Environment is a finite set E of discrete, instantaneous states: E = { e 0 , e 1 , . . . } Agents actions change the environment. Ac = { α 0 , α 1 , . . . } An agent acting in an environment generates a run, r : α 0 α 1 α 2 r : e 0 − → e 1 − → e 2 − → e 3 . . . e u An agent senses the environment state e i ∈ E and takes action α i ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary An Abstract Architecture for Agents Environment is a finite set E of discrete, instantaneous states: E = { e 0 , e 1 , . . . } Agents actions change the environment. Ac = { α 0 , α 1 , . . . } An agent acting in an environment generates a run, r : α 0 α 1 α 2 r : e 0 − → e 1 − → e 2 − → e 3 . . . e u An agent senses the environment state e i ∈ E and takes action α i ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary An Abstract Architecture for Agents Environment is a finite set E of discrete, instantaneous states: E = { e 0 , e 1 , . . . } Agents actions change the environment. Ac = { α 0 , α 1 , . . . } An agent acting in an environment generates a run, r : α 0 α 1 α 2 r : e 0 − → e 1 − → e 2 − → e 3 . . . e u An agent senses the environment state e i ∈ E and takes action α i ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary An Abstract Architecture for Agents Environment is a finite set E of discrete, instantaneous states: E = { e 0 , e 1 , . . . } Agents actions change the environment. Ac = { α 0 , α 1 , . . . } An agent acting in an environment generates a run, r : α 0 α 1 α 2 r : e 0 − → e 1 − → e 2 − → e 3 . . . e u An agent senses the environment state e i ∈ E and takes action α i ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary An Abstract Architecture for Agents Environment is a finite set E of discrete, instantaneous states: E = { e 0 , e 1 , . . . } Agents actions change the environment. Ac = { α 0 , α 1 , . . . } An agent acting in an environment generates a run, r : α 0 α 1 α 2 r : e 0 − → e 1 − → e 2 − → e 3 . . . e u An agent senses the environment state e i ∈ E and takes action α i ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Environments Define R as the set of all runs then R Ac ⊂ R that ends with an action, and R E ⊂ R that ends with an environment state A state transformer function τ : R Ac → 2 E 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 , e 0 , τ � where E is set of environment states, e 0 ∈ 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Agents An Agent is a function that maps runs to actions: Ag : R E → 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Agents An Agent is a function that maps runs to actions: Ag : R E → 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Agents An Agent is a function that maps runs to actions: Ag : R E → 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Systems 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Systems 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Systems 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 Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Traces Thus, a sequence ( e 0 , α 0 , e 1 , α 1 , e 2 , . . . ) represents a run of an agent Ag in environment Env = � E , e 0 , τ � if e 0 is the initial state of Env α 0 = Ag ( e 0 ) and for u > 0, e u ∈ τ (( e 0 , α 0 , . . . , α u − 1 )) and α u = Ag (( e 0 , α 0 , . . . , e u )) That is, E is finally in state e u as a result of action α u De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Traces Thus, a sequence ( e 0 , α 0 , e 1 , α 1 , e 2 , . . . ) represents a run of an agent Ag in environment Env = � E , e 0 , τ � if e 0 is the initial state of Env α 0 = Ag ( e 0 ) and for u > 0, e u ∈ τ (( e 0 , α 0 , . . . , α u − 1 )) and α u = Ag (( e 0 , α 0 , . . . , e u )) That is, E is finally in state e u as a result of action α u De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Traces Thus, a sequence ( e 0 , α 0 , e 1 , α 1 , e 2 , . . . ) represents a run of an agent Ag in environment Env = � E , e 0 , τ � if e 0 is the initial state of Env α 0 = Ag ( e 0 ) and for u > 0, e u ∈ τ (( e 0 , α 0 , . . . , α u − 1 )) and α u = Ag (( e 0 , α 0 , . . . , e u )) That is, E is finally in state e u as a result of action α u De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Traces Thus, a sequence ( e 0 , α 0 , e 1 , α 1 , e 2 , . . . ) represents a run of an agent Ag in environment Env = � E , e 0 , τ � if e 0 is the initial state of Env α 0 = Ag ( e 0 ) and for u > 0, e u ∈ τ (( e 0 , α 0 , . . . , α u − 1 )) and α u = Ag (( e 0 , α 0 , . . . , e u )) That is, E is finally in state e u as a result of action α u De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Traces Thus, a sequence ( e 0 , α 0 , e 1 , α 1 , e 2 , . . . ) represents a run of an agent Ag in environment Env = � E , e 0 , τ � if e 0 is the initial state of Env α 0 = Ag ( e 0 ) and for u > 0, e u ∈ τ (( e 0 , α 0 , . . . , α u − 1 )) and α u = Ag (( e 0 , α 0 , . . . , e u )) That is, E is finally in state e u as a result of action α u De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 25 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Purely Reactive Agents 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. � off if e = temperature OK action ( e ) = on otherwise De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Purely Reactive Agents 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. � off if e = temperature OK action ( e ) = on otherwise De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Purely Reactive Agents 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. � off if e = temperature OK action ( e ) = on otherwise De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
Applications of Agents Models of architecture and environment Agent characteristics Kinds of agents Agent architecture Goals and actions Summary Purely Reactive Agents 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. � off if e = temperature OK action ( e ) = on otherwise De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 26 / 35
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