BENJAMIN FRANKLIN HIRAM POWERS, SCULPTOR. Benjamin Franklin, by Hiram Powers.
Agents CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
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Transcript of Agents CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
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Agents
CPSC 386 Artificial IntelligenceEllen WalkerHiram College
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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
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A Simple Vacuum Cleaner Agent
• 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))
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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)
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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.
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Program for an Agent
• Repeat forever1. Record latest percept from sensors
into memory2. Choose best action based on memory3. Record action in memory4. Perform action (observe results)
• Almost all of AI elaborates this!
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A Reasonable Vacuum Program
• [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?
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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)
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Specifying Task Environment
• Performance measure• Environment (real world or “artificial”)• Actuators• Sensors
• Examples:– Pilot– Rat in a maze– Surgeon– Search engine
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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)
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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”?
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Examples
• Solitaire• Driving• Conversation• Chess• Internet search• Lawn mowing
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Agent Types
• Reflex• Model-based Reflex• Goal based• Utility based
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Reflex Agent
AgentEnviron-
mentsensors
effectors
world now
action now
rules
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Model-Based Reflex Agent
AgentEnviron-
mentsensors
effectors
world now
action now
rules
state
how world evolves
what actions do
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Goal Based
AgentEnviron-
mentsensors
effectors
world now
action now
goals
state
how world evolves
what actions do future world
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Utility Based
AgentEnviron-
mentsensors
effectors
world now
action now
utility
state
how world evolves
what actions do future world
"happiness"
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Learning Agent
PerformanceElement
(was agent)
Environment
Critic
LearningElement
Problem Generator
L. Goals
Feedback
Sensors
Effectors
changes
know-ledge
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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)