CSE 573 Artificial Intelligence
Dan WeldPeng Dai
www.cs.washington.edu/education/courses/cse573/08au
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Logistics:• Reading for next class
Finish R&N chapter 3 Start chapter 4 (thru 4.2)
• How do you find the book?
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Topics• Intro: overview, agents
• Search: problem spaces, heuristic generation, CSPs
• Knowledge representation: prop & 1st logic
• Planning: time, regression, SAT compile, …
• Learning: dec trees, overfitting, bias, ensembles, semi supervision
• Uncertainty: stat learning, HMMs, DBNs, Markov networks, EM
• Natural language: information extraction
• Planning under uncertainty: MDPS, reinforcement learning
• Special topics
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What is an ‘Agent’?
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Intelligent Agents• Have sensors, effectors• Implement mapping from percept
sequence to actions
• Performance measure
Environment Agent
percepts
actions
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Agent Function vs. Program• What is an ‘agent function’?
“If we can specify the agent’s choice of action for every possible percept sequence, then we have said more or less everything there is to say about the agent.” – R&N
Environment Agent
percepts
actions
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Are Toasters Agents?
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Defn: Ideal rational agent
• Rationality vs omniscience?• Acting in order to obtain valuable information
“For each possible percept sequence, does
whatever action is expected to maximize its
performance measure on the basis of evidence
perceived so far and built-in knowledge.''
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Defn: Autonomy
An agent is autonomous to the extent that its behavior is determined by its own experience
Why is this important?
The parable of the dung beetle
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Implementing ideal rational agent
• Table lookup agents• Agent program
Simple reflex agents Agents with memory
•Reflex agent with internal state•Goal-based agents•Utility-based agents
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Simple reflex agentsE
NV
IRO
NM
EN
T
AGENT
Effectors
Sensors
what world islike now
Condition/Action ruleswhat action should I do now?
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Reflex agent with internal state
EN
VIR
ON
ME
NT
AGENT Effectors
Sensors
what world islike now
Condition/Action ruleswhat action should I do now?
What world was like
How world evolves
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Goal-based agentsE
NV
IRO
NM
EN
T
AGENT Effectors
Sensors
what world islike now
Goalswhat action should I do now?
What world was like
How world evolves
what it’ll be likeif I do acts A1-An
What my actions do
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Utility-based agentsE
NV
IRO
NM
EN
T
AGENT Effectors
Sensors
what world islike now
Utility functionwhat action should I do now?
What world was like
How world evolves
What my actions do
How happy would I be?
what it’ll be likeif I do acts A1-An
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Properties of Environments
• Observability: full vs. partial vs. non
• Deterministic vs. stochastic• Episodic vs. sequential• Static vs. … vs. dynamic• Discrete vs. continuous
• Travel agent
• WWW shopping agent
• Coffee delivery mobile robot