1. CSE 573 Artificial Intelligence Dan Weld Peng Dai www.cs.washington.edu/education/courses/cse573/08au

2. © Daniel S. Weld 2 Logistics: Reading for next class Finish R&N chapter 3 Start chapter 4 (thru 4.2) How do you find the book?

3. © Daniel S. Weld 3 Topics Intro: overview, agents Search: problem spaces, heuristic generation, CSPs Knowledge representation: prop & 1 st 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

4. © Daniel S. Weld 4 What is an ‘Agent’?

5. © Daniel S. Weld Intelligent Agents Have sensors, effectors Implement mapping from percept sequence to actions Performance measure Environment Agent percepts actions

6. © Daniel S. Weld 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

7. © Daniel S. Weld 7 Are Toasters Agents?

8. © Daniel S. Weld 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.''

9. © Daniel S. Weld 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

10. © Daniel S. Weld 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

11. © Daniel S. Weld Simple reflex agents ENVIRONMENT AGENT Effectors Sensors what world is like now Condition/Action rules what action should I do now?

12. © Daniel S. Weld Reflex agent with internal state ENVIRONMENT AGENTEffectors Sensors what world is like now Condition/Action rules what action should I do now? What world was like How world evolves

13. © Daniel S. Weld Goal-based agents ENVIRONMENT AGENT Effectors Sensors what world is like now Goals what action should I do now? What world was like How world evolves what it’ll be like if I do acts A 1 -A n What my actions do

14. © Daniel S. Weld Utility-based agents ENVIRONMENT AGENT Effectors Sensors what world is like now Utility function what 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 like if I do acts A 1 -A n

15. © Daniel S. Weld 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