Transcript Agents CPSC 386 Artificial Intelligence Ellen Walker Hiram College Agents • An agent perceives its environment through sensors, and acts upon it through actuators. • The.

Agents
CPSC 386 Artificial Intelligence
Ellen Walker
Hiram College
Agents
• An agent perceives its environment through
sensors, and acts upon it through actuators.
• The agent’s percepts are its impression of the
sensor input.
• (The agent doesn’t necessarily know
everything in its environment)
• Agents may have knowledge and/or memory
A Simple Vacuum Cleaner Agent
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2 Locations, A and B
Dirt sensor (current location only)
Agent knows where it is
Actions: left, right, suck
• “Knowledge” represented by percept, action
pairs
(e.g. [A, dirty] -> (suck))
Agent Function vs. Agent Program
• Agent function:
– Mathematical abstraction f(percepts) = action
– Externally observable (behavior)
• Agent program:
– Concrete implementation of an algorithm that
decides what the agent will do
– Runs within a “physical system”
– Not externally observable (thought)
Rational Agents
• Rational Agents “do the right thing” based on
– Performance measure that defines criterion of
success
– The agent’s prior knowledge of the environment
– Actions that the agent can perform
– Agent’s percept sequence to date
• Rationality is not omniscience; it optimizes
expected performance, based on
(necessarily) incomplete information.
Program for an Agent
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Repeat forever
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Record latest percept from sensors into memory
Choose best action based on memory
Record action in memory
Perform action (observe results)
Almost all of AI elaborates this!
A Reasonable Vacuum Program
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[A, dirty] -> suck
[B, dirty] -> suck
[A, clean] -> right
[B, clean] -> left
• What goals will this program satisfy?
• What are pitfalls, if any?
• Does a longer history of percepts help?
Aspects of Agent Behavior
• Information gathering - actions that modify
future percepts
• Learning - modifying the program based on
actions and perceived results
• Autonomy - agent’s behavior depends on its
own percepts, rather than designer’s
programming (a priori knowledge)
Specifying Task Environment
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Performance measure
Environment (real world or “artificial”)
Actuators
Sensors
• Examples:
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Pilot
Rat in a maze
Surgeon
Search engine
Properties of Environments
• Fully vs. partially observable (e.g. map?)
• Single-agent vs. multi-agent
– Adversaries (competitive)
– Teammates (cooperative)
• Deterministic vs. stochastic
– May appear stochastic if only partially observable
(e.g. card game)
– Strategic: deterministic except for other agents
• (Uncertain = not fully observable, or
nondeterministic)
Properties (cont)
• Episodic vs. Sequential
– Do we need to know history?
• Static vs. Dynamic
– Does environment change while agent is thinking?
• Discrete vs. Continuous
– Time, space, actions
• Known vs. Unknown
– Does the agent know the “rules” or “laws of
physics”?
Examples
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Solitaire
Driving
Conversation
Chess
Internet search
Lawn mowing
Agent Types
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Reflex
Model-based Reflex
Goal based
Utility based
Reflex Agent
Agent
sensors
world now
rules
action now
effectors
Environment
Model-Based Reflex Agent
Agent
sensors
state
world now
how w orld evolves
w hat actions do
rules
action now
effectors
Environment
Goal Based
Agent
sensors
state
world now
how w orld evolves
w hat actions do
future world
goals
action now
effectors
Environment
Utility Based
Agent
sensors
state
world now
how w orld evolves
w hat actions do
future world
utility
"happiness"
action now
effectors
Environment
Learning Agent
Critic
Feedback
Sensors
changes
Learning
Element
L. Goals
Problem
Generator
Performance
Element
know(was agent)
ledge
Effectors
Environment
Classes of Representations
• Atomic
– State is indivisible
• Factored
– State consists of attributes and values
• Structured
– State consists of objects (which have attributes
and relate to other objects)