1. Agents & Environments

2. © Daniel S. Weld 2 473 Topics Agents & Environments Problem Spaces Search & Constraint Satisfaction Knowledge Repr’n & Logical Reasoning Machine Learning Uncertainty: Repr’n & Reasoning Dynamic Bayesian Networks Markov Decision Processes

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

4. © Daniel S. Weld Types of Agents: Robots Xavier (CMU) Aibo (Sony) Walking (MIT) Humanoid - Cog (MIT)

5. © Daniel S. Weld Types of Agents: Immobots Intelligent buildings Autonomous spacecraft Softbots Jango.excite.com Askjeeves.com Expert Systems Cardiologist

6. © Daniel S. Weld Types of Agents: Immobots Autonomous spacecraft Intelligent buildings

7. © Daniel S. Weld 7 Rolling Robots Xavier (CMU)MIT Mobile Robot Group

8. © Daniel S. Weld 8 Driving Robots DAWN (CMU Robotics Lab)

9. © Daniel S. Weld 9 Walking Robots Quadraped (MIT Leg Lab) Flamingo (MIT Leg Lab)

10. © Daniel S. Weld 10 Humanoid Robots Cog Project (MIT)

11. © Daniel S. Weld 11 Software Robots

12. © 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.'' “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.''

13. © 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

14. © 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

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

16. © 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

17. © 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

18. © 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

19. © Daniel S. Weld Properties of Environments Observability: full vs. partial vs. non Deterministic vs. stochastic Episodic vs. nonepisodic Static vs. … vs. dynamic Discrete vs. continuous Travel agent WWW shopping agent Coffee delivery mobile robot

20. © Daniel S. Weld While driving, what’s the best policy? Always put blinker on before turning Never use your blinker Look in mirror, and use blinker only if you observe a car that can observe you What kind of reasoning? –logical, goal-based, utility-based? What kind of agent is necessary? –reflex, goal-based, utility-based?