1. Plans for Today Chapter 2: Intelligent Agents (until break) Lisp: Some questions that came up in lab Resume intelligent agents after Lisp issues

2. Intelligent Agents Agent: anything that can be viewed as… perceiving its environment through sensors acting upon its environment through actuators Examples: Human Web search agent Chess player What are sensors and effectors for each of these?

3. Rational Agents Conceptually: one that does the right thing Criteria: Performance measure Performance measures for Web search engine? Tic-tac-toe player? Chess player? When performance is measured plays a role short vs. long term

4. Rational Agents Omniscient agent Knows actual outcome of its actions What info would chess player need to be omniscient? Omniscience is (generally) impossible Rational agent should do right thing based on knowledge it has

5. Rational Agents What is rational depends on four things: Performance measure Percept sequence: everything agent has seen so far Knowledge agent has about environment Actions agent is capable of performing Rational Agent definition: Does whatever action is expected to maximize its performance measure, based on percept sequence and built-in knowledge

6. Autonomy “Independence” A system is autonomous if its behavior is determined by its percepts An alarm that goes off at a prespecified time is not autonomous An alarm that goes off when smoke is sensed is autonomous A system without autonomy lacks flexibility

7. The Task Environment An agent’s rationality depends on Performance Measure Environment Actuators Sensors What are each of these for: Chess Player? Web Search Tool? Matchmaker? Musical performer?

8. Environments: Fully Observable vs. Partially Observable Fully observable: agent’s sensors detect all aspects of environment relevant to deciding action Examples? Which is more desirable?

9. Environments: Determinstic vs. Stochastic Deterministic: next state of environment is completely determined by current state and agent actions Stochastic: uncertainty as to next state If environment is partially observable but deterministic, may appear stochastic If environment is determinstic except for actions of other agents, called strategic Agent’s point of view is the important one Examples? Which is more desirable?

10. Environments: Episodic vs. Sequential Episodic: Experience is divided into “episodes” of agent perceiving then acting. Action taken in one episode does not affect next one at all. Sequential typically means need to do lookahead Examples? Which is more desirable?

11. Environments: Static vs. Dynamic Dynamic: Environment can change while agent is thinking Static: Environment does not change while agent thinks Semidynamic: Environment does not change with time, but performance score does Examples? Which is more desirable?

12. Environments: Discrete vs. Continuous Discrete: Percepts and actions are distinct, clearly defined, and often limited in number Examples? Which is more desirable?

13. Environments: Single agent vs. multiagent What is distinction between environment and another agent? for something to be another agent, maximize a performance measure depending on your behavior Examples?

14. Structure of Intelligent Agents What does an agent program look like? Some extra Lisp: Persistence of state (static variables) Allows a function to keep track of a variable over repeated calls. Put functions inside a let block (let ((sum 0)) (defun myfun (x) (setf sum (+ sum x))) (defun report () sum) )

15. Generic Lisp Code for an Agent (let ((memory nil)) (defun skeleton-agent (percept) (setf memory (update-memory memory percept)) (setf action (choose-best-action memory)) (setf memory (update-memory memory action)) action ; return action ))

16. Table Lookup Agent In theory, can build a table mapping percept sequence to action Inputs: percept Outputs: action Static Variable: percepts, table

17. Lookup Table Agent (let ((percepts nil) (table ????) (defun table-lookup-agent (percept) (setf percepts (append (list percept) percepts)) (lookup percepts table)) ))

18. Specific Agent Example: Pathfinder (Mars Explorer) Performance Measure: Environment: Actuators: Sensors: Would table-driven work?

19. Four kinds of better agent programs Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents

20. Simple reflex agents Specific response to percepts, i.e. condition-action rule if new-boulder-in-sight then move-towards-new-boulder Advantages: Disadvantages:

21. Model-based reflex agents Maintain an internal state which is adjusted by each percept Internal state: looking for a new boulder, or rolling towards one Affects how Pathfinder will react when seeing a new boulder Can be used to handle partial observability by use of a model about the world Rule for action depends on both state and percept Different from reflex, which only depends on percept

22. Goal-Based Agents Agent continues to receive percepts and maintain state Agent also has a goal Makes decisions based on achieving goal Example Pathfinder goal: reach a boulder If pathfinder trips or gets stuck, can make decisions to reach goal

23. Utility-Based Agents Goals are not enough – need to know value of goal Is this a minor accomplishment, or a major one? Affects decision making – will take greater risks for more major goals Utility: numerical measurement of importance of a goal A utility-based agent will attempt to make the appropriate tradeoff

24. Lisp Questions

25. Why the dot in cons? Two Explanations: High level: cons expects a list in the second position Lower level: Cons takes a cons cell from the free storage list Puts first argument in “first” position Puts second argument in “rest” position Separates by a dot, unless “rest” position is a pointer (indicates continuing list)

26. How does append work? Makes copy of first list Takes last pointer and points to second list Picture

27. How to debug? Can trace function calls with (trace function) and (untrace function) Demonstration with mystery function from lab At a Break> prompt, can see call stack with backtrace Can go through code step by step (step (mystery 2 3)) Use step and next to go through each function as you go along Use (print var)

28. Random bits Why the p in (zerop x)? p = predicate NOT true that p = positive

29. Scoping and binding let declares a scope where variable bindings are insulated from outside usual notions of local and global variables apply if you want to change a global variable from within a function