SLIDE 1
Intelligent Agents
Christian Jacob TOC
1
Back
Defining Agents Christian Jacob First Back TOC
2
Prev Next Last
1 Defining Agents 2 2 How Agents Should Act 3 2.1 Mapping from - - PowerPoint PPT Presentation
1 Defining Agents 2 2 How Agents Should Act 3 2.1 Mapping from - - PowerPoint PPT Presentation
Intelligent Agents Christian Jacob 1 Defining Agents 2 2 How Agents Should Act 3 2.1 Mapping from Percept Sequences to Actions 5 2.2 Autonomy 6 3 Designs of Intelligent Agents 7 3.1 Architecture and Program 7 3.2 Agent Programs
SLIDE 2
SLIDE 3
How Agents Should Act Christian Jacob First Back TOC
3
Prev Next Last
2 How Agents Should Act
Rational Agent
A rational agent performs actions that cause the agent to be most successful, depending on a performance measure, which decides how and when to evaluate an agent. What is rational at any given time depends on four things:
- The performance measure that defines degree of success
- The percept sequence, a complete history of what the agent has perceived so
far
- The agent’s knowledge about the environment
- The actions that the agent can perform
This leads to a definition of an ideal rational agent ...
SLIDE 4
How Agents Should Act Christian Jacob First Back TOC
4
Prev Next Last
Ideal Rational Agent
For each possible percept sequence, an ideal rational agent should do
- whatever action is expected to maximize its performance measure,
- on the basis of the evidence provided by the percept sequence and
- whatever built-in knowledge the agent has.
Example: Crossing a busy road Doing actions in order to obtain useful information is an important part of rationality. The notion of an agent is meant to be a tool for analyzing systems.
SLIDE 5
How Agents Should Act Christian Jacob First Back TOC
5
Prev Next Last
2.1 Mapping from Percept Sequences to Actions
Any particular agent can be described by making a table of the action it takes in response to each possible percept sequence. Such a list is called a mapping from percept sequences to actions. However, this does not mean that we have to create an explicit table with an entry for every possible percept sequence (compare the square root example).
Percept x Action z 1.0 1.000000 1.1 1.048808 1.2 1.095445 1.3 1.140175 1.4 1.183215 1.5 1.224744 1.6 1.264911 1.7 1.303840 1.8 1.341640 1.9 1.378404 . . . function SQRT(x) z := 1.0 /* initial guess */ repeat until | z2 - x | < 10-15 z := z - (z2 - x) / ( 2 z ) end return z
SLIDE 6
How Agents Should Act Christian Jacob First Back TOC
6
Prev Next Last
2.2 Autonomy
Assume an agent’s actions are based completely on built-in knowledge. Then it need not pay attention to its percepts. This agent clearly lacks autonomy. An agent’s behavior can be based on both its
- own experience and
- built-in knowledge
A system is autonomous to the extent that its behavior is determined by its own experience.
- -> Agents should be provided with
- initial knowledge (compare animal reflexes) and
- the ability to learn.
SLIDE 7
Designs of Intelligent Agents Christian Jacob First Back TOC
7
Prev Next Last
3 Designs of Intelligent Agents
AI intends to design agent programs, functions that implement the agent mapping from percepts to actions. The computing device, we assume this program to run on, is referred to as the architecture.
3.1 Architecture and Program
The architecture might include special-purpose hardware (camera, microphone,etc.). The software might provide a degree of insulation between the raw computer hardware and the agent program, enabling programming at a higher level. agent = architecture + program What matters is not the distinction between “real” and “artificial” environments, but the complexity of the relationship among the behavior of the agent, the percept sequence generated by the environment, and the goals that the agent is supposed to achieve.
SLIDE 8
Designs of Intelligent Agents Christian Jacob First Back TOC
8
Prev Next Last
Percepts and Actions for a Selection of Agent Types
Agent Type Percepts Actions Goals Environment Medical diagno- sis system Symptoms, find- ings, patient’s answers Questions, tests, treatments Healthy patient, minimize cost Patient, hospital Satellite image analysis system Pixels of varying intensity, color Print a categoriza- tion of scene Correct catego- rization Image from
- rbiting satellite
Part-picking robot Pixels of varying intensity Pick up parts and sort into bins Place parts in correct bins Conveyor belt with parts Refinery control- ler Temperature, pressure readings Open, close valves; adjust temperature Maximizing purity, yield, safety Refinery Interactive English tutor Typed words Print exercises, suggestions, corrections Maximize stu- dent’s score on test Set of students
SLIDE 9
Designs of Intelligent Agents Christian Jacob First Back TOC
9
Prev Next Last
3.2 Agent Programs
All agents and agent programs accept percepts from an environment and generate actions. function SKELETON-AGENT( percept ) returns action static: memory the agent’s memory of the world memory <--- UPDATE-MEMORY( memory, percept ) action <--- CHOOSE-BEST-ACTION( memory ) memory <--- UPDATE-MEMORY( memory, action ) return action
SLIDE 10
Designs of Intelligent Agents Christian Jacob First Back TOC
10
Prev Next Last
Remarks on agent programs:
Percept sequence
- The agent program receives only a single percept as its input.
- It is up to the agent to build up the percept sequence in memory.
Performance measure
- The goal or performance measure is not part of the agent program.
- The performance evaluation is applied externally.
SLIDE 11
Designs of Intelligent Agents Christian Jacob First Back TOC
11
Prev Next Last
3.3 Simple Lookup?
A lookup table is the simplest possible way of writing an agent program. It operates by keeping in memory its entire percept sequence, and using it to index into table, which contains the appropriate action for all possible percept sequences. function TABLE-DRIVEN-AGENT( percept ) returns action static: percepts, a sequence, initially empty table, a table, indexed by percept sequences, initially fully specified append percept to the end of percepts action <--- LOOKUP( percepts, table ) return action
SLIDE 12
Designs of Intelligent Agents Christian Jacob First Back TOC
12
Prev Next Last
Why is this TABLE-DRIVEN AGENT proposal doomed to failure?
- Table size:
The table needed for something as simple as an agent that can only play chess would be about 35100 entries.
- Time to build:
It would take an enormous amount of time to build complete tables.
- Lack of autonomy:
The agent has no autonomy at all, because the calculation of best actions is entirely built-in. If the environment changed in some unexpected way, the agent would be lost.
- Lack or infeasibility of learning:
Even if the agent were given a learning mechanism, so that it could have a degree of autonomy, it would take forever to learn the right value for all the table entries.
SLIDE 13
Designs of Intelligent Agents Christian Jacob First Back TOC
13
Prev Next Last
3.4 Example — An Automated Taxi Driver
The full driving task is extremely open-ended. There is no limit to the novel combination of circumstances that can arise. First, we have to think about the percepts, actions, goals and environment for the taxi.
Agent Type Percepts Actions Goals Environment Taxi driver Cameras, speedometer, GPS, sonar, microphone Steer, accelerate, brake, talk to passenger, communicate with
- ther vehicles
Safe, fast, legal, comfortable trip, maximize profits Roads,
- ther traffic,
pedestrians, customers
SLIDE 14
Designs of Intelligent Agents Christian Jacob First Back TOC
14
Prev Next Last
Performance measures for the taxi driver agent:
- Getting to the correct destination
- Minimizing fuel consumption and wear and tear
- Minimizing the trip time and/or cost
- Minimizing violations of traffic laws
- Minimizing disturbances to other drivers
- Maximizing safety and passenger comfort
- Maximizing profits
Obviously, some of these goals conflict, so there will be trade-offs involved. Driving environments:
- local roads or highways
- weather conditions
- left or right lane driving
- …
SLIDE 15
Types of Agents Christian Jacob First Back TOC
15
Prev Next Last
4 Types of Agents
We have to decide how to build a real program to implement the mapping from percepts to action for the taxi driver agent. Different aspects of driving suggest different types of agent programs. We will consider four types of agent programs:
- Simple reflex agents
- Agents that keep track of the world
- Goal-based agents
- Utility-based agents
SLIDE 16
Types of Agents Christian Jacob First Back TOC
16
Prev Next Last
4.1 Simple Reflex Agents
Instead of constructing a lookup table for the percept-action mapping, we can summarize portions of the table by noting certain commonly occurring input/
- utput associations.
This can be accomplished by condition-action rules. Example: if car-in-front-is-breaking then initiate-braking Humans (and animals in general) have many such connections,
- some of which are learned responses (e.g., driving) and
- some of which are innate reflexes.
SLIDE 17
Types of Agents Christian Jacob First Back TOC
17
Prev Next Last
Schematic diagram of a simple reflex agent
Environment Agent
Sensors Effectors What the world is like now What action I should do now Condition-action rules
SLIDE 18
Types of Agents Christian Jacob First Back TOC
18
Prev Next Last
A simple reflex agent
function SIMPLE-REFLEX-AGENT( percept ) returns action static: rules, a set of condition-action rules state <-- INTERPRET-INPUT( percept ) rule <-- RULE-MATCH( state, rules ) action <-- RULE-ACTION[ rule ] return action Works only if the correct decision can be made on the basis of the current percept.
SLIDE 19
Types of Agents Christian Jacob First Back TOC
19
Prev Next Last
4.2 Agents that Keep Track of the World
For determining whether a vehicle is braking one has to keep the previous frame from the camera to detect when two red lights at the edge of the vehicle go on or off simultaneously. Hence, the driving agent will have to maintain some sort of internal state. Two kinds of knowledge have to be encoded in the agent program:
- Information about how the world evolves independently of the agent.
- Information about how the agent´s own actions will affect the world.
SLIDE 20
Types of Agents Christian Jacob First Back TOC
20
Prev Next Last
Schematic diagram of a reflex agent with internal state
Environment Agent
Sensors Effectors What the world is like now What action I should do now Condition-action rules State How the world evolves What my actions do
SLIDE 21
Types of Agents Christian Jacob First Back TOC
21
Prev Next Last
A reflex agent with internal state
function REFLEX-AGENT-WITH-STATE( percept ) returns action static: state, a description of the current world state rules, a set of condition-action rules state <-- UPDATE-STATE( state, percept ) rule <-- RULE-MATCH( state, rules ) action <-- RULE-ACTION[ rule ] state <-- UPDATE-STATE( state, action ) return action However, knowing about the current state of the environment is not always enough to decide what to do.
SLIDE 22
Types of Agents Christian Jacob First Back TOC
22
Prev Next Last
4.3 Goal-Based Agents
Besides a current state description the agent needs some sort of goal information, which describes situations that are desirable. The agent program can combine this with information about the results of possible actions in order to choose actions that achieve the goal. Achieving the goal may involve
- a single action or
- (long) sequences of actions.
The subfields of Ai devoted to finding action sequences that do achieve agent´s goals are
- searching and
- planning.
Goal-based agents involve consideration of the future and is more flexibly reacting to changes in the environment.
SLIDE 23
Types of Agents Christian Jacob First Back TOC
23
Prev Next Last
Schematic diagram of an agent with explicit goals
Environment Agent
Sensors Effectors What the world is like now What action I should do now Goals State How the world evolves What my actions do What it will be like if I do action A
SLIDE 24
Types of Agents Christian Jacob First Back TOC
24
Prev Next Last
4.4 Utility-Based Agents
Goals alone are not really enough to generate high-quality behavior. There might be different ways (action sequences) of achieving a specific goal. If one world state is preferred to another, then it has higher utility for the agent. Utility is therefore a function that maps a state onto a real number, which describes the associated degree of “happiness”. A complete specification of the utility function allows rational decisions in two kinds of cases:
- When there are conflicting goals, only some of which can be achieved, the
utility function specifies the appropriate trade-off.
- When there are several goals that the agent can aim for, none of which can be
achieved with certainty, utility provides a way in which the likelihood of success can be weighed up against the importance of the goals.
SLIDE 25
Types of Agents Christian Jacob First Back TOC
25
Prev Next Last
Schematic diagram of a complete utilty-based agent
Environment Agent
Sensors Effectors What the world is like now What action I should do now Utility State How the world evolves What my actions do What it will be like if I do action A How happy I will be in such a state
SLIDE 26
Environments Christian Jacob First Back TOC
26
Prev Next Last
5 Environments
5.1 Properties of Environments
- Accessible vs. inaccessible.
An agent´s sensory apparatus gives it access to the complete state of the environment. An environment is effectively accessible if the sensors detect all aspects that are relevant to the choice of action. In an accessible environment an agent need not maintain any internal state to keep track of the world.
- Deterministic vs. nondeterministic
In a deterministic environment, the next state of the environment is completely determined by the current state and the actions selected by the agents. An agent need not worry about uncertainty in an accessible, deterministic environment. If the environment is inaccessible, however, it may appear to the agent to be nondeterministic.
SLIDE 27
Environments Christian Jacob First Back TOC
27
Prev Next Last
- Episodic vs. nonepisodic
In an episodic environment, the agent´s experience is divided into “episodes”. Each episode consists of the agent perceiving and the acting. Subsequent episodes do not depend on what actions occur in previous episodes. In episodic environments the agent does not have to think ahead.
- Static vs. dynamic
A dynamic environment can change while an agent is deliberating. In static environments, an agent need not keep looking at the world while it is deciding on an action, nor need it worry about the passage of time. An environment is called semidynamic if it does not change with the passage
- f time but the agent´s performance score does.
- Discrete vs. continuous
If there are a limited number of distinct, clearly defined percepts and actions we say that the environment is discrete. Chess is discrete. Taxi driving is continuous.
SLIDE 28
Environments Christian Jacob First Back TOC
28
Prev Next Last
Examples of Environments and their Characteristics
Environment Accessible Deterministic Episodic Static Discrete Chess with a clock Chess without a clock Poker Backgammon Taxi driving Medical diagnosis system Image-analysis system Part-picking robot Refinery controller Interactive English tutor Yes Yes No Yes No No Yes No No No Yes Yes No No No No Yes No No No No No No No No No Yes Yes No No Semi Yes Yes Yes No No Semi No No No Yes Yes Yes Yes No No No No No Yes
SLIDE 29
Environments Christian Jacob First Back TOC
29
Prev Next Last
5.2 Environment Programs
A generic environment program illustrates the basic relationship between agents and environments. procedure RUN-ENVIRONMENT( state, UPDATE-FN, agents, termination ) inputs: state the initial state of the environment UPDATE-FN function to modify the environment agents a set of agents termination a predicate to test when we are done repeat for each agent in agents do PERCEPT[ agent ] <--- GET-PERCEPT( agent, state ) end for each agent in agents do ACTION[ agent ] <--- PROGRAM[ agent ]( PERCEPT[ agent ]) end state <--- UPDATE-FN( actions, agents, state ) until termination( state )
SLIDE 30
Environments Christian Jacob First Back TOC
30
Prev Next Last