This document summarizes key concepts in artificial intelligence planning and logic. It discusses representations like atomic, factored, and structured states. Planning approaches include state-space search, planning graphs, and situation calculus. Factored representations allow more flexible and hierarchical plans using relations between state variables. Planning graphs efficiently represent possible plan states and actions to derive heuristic estimates and extract plans.

1. CS 221: Artificial Intelligence Planning (and Basic Logic) Peter Norvig and Sebastian Thrun Slide credits: Stuart Russell, Rina Dechter, Rao Kambhampati

3. Finding Actions

4. What’s wrong with Problem-Solving Plan: [Forward, Forward, Forward, …]

5. What’s wrong with Problem-Solving

7. Dealing with Partial Observability: World vs. Belief States

8. Sensorless – Belief States - Conformant Plans

9. Deterministic world Slippery wheels Partial (local) Observability and Stochastic Worlds Deterministic actions; observe only local square Observe only local square; Suck is determ. R/L are stoch. (may fail to move)

10. Slippery wheels Planning and Sensing in Partially Observable and Stochastic World What is a plan to achieve all states clean? [1: Suck ; 2: Right ; ( if A : goto 2); 3: Suck ] also written as [ Suck ; ( while A : Right ); Suck ] Observe only local square; Suck is determ. R/L are stoch.; may fail to move

11. Search Graph as And/Or Tree What do we need to guarantee success? What kind of guarantee?

13. Kindergarten world: dirt may appear anywhere at any time, But actions are guaranteed to work. b1 b3 =UPDATE( b1 ,[ A,Clean ]) b5 = UPDATE( b4,… ) b2 =PREDICT( b1, Suck ) b4 =PREDICT( b3 , Right )

14. State Representation

19. Expressiveness of the language

22. Plan-space search

23. Plan-space search Start Finish Left Sock Finish Start Right Shoe Finish Start Right Shoe Finish Start Left Shoe

24. Plan-space search

27. 8-puzzle state space

29. 8-puzzle heuristics

30. Convex search: ignore del lists

31. Factored Rep allows control

32. Factored Rep allows control

37. Situations as Result of Action Situation Calculus First-order Logic ∃s, p : Goal(s) ∧ s = Result(s0, p) s = Result(s, []) Result(s, [a,b,…]) = Result(Result(s, a), [b,…])

42. Planning Graph Example Create level 0 from initial problem state.

43. Planning Graph Example Add all applicable actions. Add all effects to the next state.

44. Planning Graph Example Add persistence actions (inaction = no-ops) to map all literals in state S i to state S i+1 .

45. Planning Graph Example Identify mutual exclusions between actions and literals based on potential conflicts.