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D Goforth - COSC 4117, fall 2006 2
The agent model
agents include all aspects of AI in one object-oriented organizing model:
e n v i r o n m e n t
AGENT
act perceive
purpose
D Goforth - COSC 4117, fall 2006 4
Purpose
Why do agents act? goals
internal (state of agent’s structure, eg survive)
external (state of environment, eg clean up dirt)
How to measure success? compare actual results to goals R&N ‘performance measure’
D Goforth - COSC 4117, fall 2006 5
Performance measure external to agent (like javadoc
specification) ideal that cannot always be achieved
completely (unlike javadoc specs)Agent success (‘rationality’) is evaluated
based on performance measure ANDpercepts, possible actions, experience(like an athlete)
D Goforth - COSC 4117, fall 2006 6
Factors in rationality
performance measure – goals may be in conflict – can’t all be achieved
perceptions – agent may not have all the facts
actions available experience – agent may not yet have
accumulated all available relevant data
D Goforth - COSC 4117, fall 2006 7
Agents are not just methods
actual outcome of actions are not known 100%
algorithms are not complete solutions – agents should be partly autonomous learn from experience gather data about environment respond better to same perceptions
D Goforth - COSC 4117, fall 2006 8
The agent model
agents include all aspects of AI in one organizing model:
e n v i r o n m e n t
AGENT
act perceive
purpose
D Goforth - COSC 4117, fall 2006 9
ExampleCASH REGISTER AS AGENT
Goals: get payment for items, update inventory, accumulate payments
Perceive bar code Know price lists Understand finding prices, names from bar code Understand accumulating bill Act to send price, code to accounting, send inventory
change to db. Act to display item name, price, and running total Perceive signal for no-more-items Act to request payment Perceive payment …
D Goforth - COSC 4117, fall 2006 10
Environments
real or virtual may contain other agents
• factors relevant to the agent are called the state of the environment
• perceptions give agent information about the state
• actions of agent change the state
D Goforth - COSC 4117, fall 2006 11
Categorizing Environments(R&N p40-44)
1. fully or partly observable – perception of state of the environment
game examples: chess, bridge, Myst
D Goforth - COSC 4117, fall 2006 12
Categorizing Environments(R&N p40-44)
2. actions are predictable – deterministic vs. stochastic vs. strategic
game examples: chess, Monopoly, solitaire yogo peg game, solitaire card game
D Goforth - COSC 4117, fall 2006 13
Categorizing Environments(R&N p40-44)
3. episodic vs. sequential – actions are based on how many previous perceptions and actions?
game examples: chess, paper-scissors-rock, bridge trick, bridge hand
D Goforth - COSC 4117, fall 2006 14
Categorizing Environments(R&N p40-44)
4. real-time vs event driven- (static vs dynamic) agent and environment are sequential or co-routines
game examples: chess, tetris
D Goforth - COSC 4117, fall 2006 15
Categorizing Environments(R&N p40-44)
5. discrete vs. continuousenvironment, perception, action
game examples: chess, tetris, driving simulator
D Goforth - COSC 4117, fall 2006 16
Categorizing Environments(R&N p40-44)
6. number of agents – 1 or morecompetitive, cooperative, codependent, interfering, communicating (info separate from perceptions)
game examples: solitaires, chess, bridge, futures, tetris, driving simulator(s), role playing games
D Goforth - COSC 4117, fall 2006 17
Categorizing King’s Court:
1. fully / partly observable2. deterministic / stochastic3. sequential / episodic4. static / dynamic5. discrete / continuous6. single- / multi-agent
D Goforth - COSC 4117, fall 2006 18
Agent Structure
agent program is ‘episodic’ – receives percepts and produces actions (parameters and return values)
BUTinternal state of agent can evolve sequentially – agent may be in a different state after episode than before
D Goforth - COSC 4117, fall 2006 19
Agent Structure
1. Table-Driven (p.45) single perception look-up (HUGE table) perception sequence look-up (HUGER
table) example game: tic-tac-toe perfect solution but intractible
D Goforth - COSC 4117, fall 2006 20
Table-driven agents (revised from R&N)
LOOK-UP TABLE
Key value
Percept1 action1
Percept2 action2
…
KNOWLEDGE
D Goforth - COSC 4117, fall 2006 21
Agent Structure
2. Simple reflex (p.46) based on current perception only i.e., no instance variables in the agent
object; no state ‘condition-action’ rules (if then else
algorithm)
D Goforth - COSC 4117, fall 2006 23
Agent Structure
3. Model-based reflex (p.48) uses percepts to build internal model of
environment - internal state is ‘memory’ of
environment algorithm based on percepts and
internal state
D Goforth - COSC 4117, fall 2006 25
Agent Structure
4. Goal-based (p.49) internal state representing environmentPLUS goals expressed in terms of environment
and/or agent states NOT REFLEX; ‘tries’ actions internally
and tests results against goals
D Goforth - COSC 4117, fall 2006 27
Agent Structure
5. Utility-based (p.50) internal state representing environment
PLUS goals expressed in terms of environment
and/or agent states
PLUS performance measure rationality ‘tries’ actions internally and tests results
against goals AND performance measure
D Goforth - COSC 4117, fall 2006 29
Agent Structure
6. Learning(p.50) extra component to evaluate
performance and change program (if necessary) to act differently in same state
many kinds of learning agents
D Goforth - COSC 4117, fall 2006 31
Agent Structures
1. Table driven2. Simple reflex3. Model-based reflex4. Goal-based5. Utility-based6. Learning
D Goforth - COSC 4117, fall 2006 32
ExampleCASH REGISTER AS AGENT:
Goals: get payment for items, update inventory, accumulate payments
Perceive bar code Know price lists Understand finding prices, names from bar code Understand accumulating bill Act to send price, code to accounting, send
inventory change to db. Act to display item name, price, and running
total Act to request payment Perceive payment …