Lecture12HO - Classical Planning" CS440/ECE448: Intro...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 12: Planning algorithms Prof. Julia Hockenmaier juliahmr@illinois.edu http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence State transition system ! = (S,A, ! ) Classical Planning Planner Solution (= sequence of actions) (a 1 ,a 2 ,…,a n-1 ,a n ) Initial state s 0 Goal speciFcation (description of goal states) S g Operators Review: representations for planning Situation Calculus Strips Specify ±uents Add -set Persist -set Specify ±uents Add -set Delete -set By default ±uents are deleted By default ±uents persist Sussman anomaly B A C Start C B A Goal Start: On(C,A) Goal: On(A,B) ˭ On(B,C)
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
B A C C B A Solve On(A,B) first: B A C B A C B A C A C B C B A Start: On(C,A) Goal: On(A,B) ˭ On(B,C) B A C C B A Solve On(B,C) first: A C B B A C A C B C B A A C B Start: On(C,A) Goal: On(A,B) On(B,C) B A C C B A Most efficient solution requires interleaved planning: B A C A C B C B A Start: On(C,A) Goal: On(A,B) On(B,C) Planning algorithms State space search (DFS, BFS, etc.) Nodes = states; edges = actions; Heuristics (make search more ef±cient) Compute h() using relaxed version of the problem Plan space search (re±nement of partial plans) Nodes = partial plans; edges: ±x ²aws in plan SATplan (encode plan in propositional logic) Solution = true variables in a model for the plan Graphplan (reduce search space to planning graph) Planning graph: levels = literals and actions 8 CS440/ECE448: Intro AI
Background image of page 2
State space search I I,a2,a34 Planning as state space search I,a2 I,a17 I,a4 I,a15 Search tree: Nodes: states Root: initial state Edges: actions (ground instances of operators Solutions: paths from initial state to goal. I,a4,a3 I,a15,a4 Forward search Breadth-Frst forward search is sound and complete, but may require lots of memory Depth-Frst forward search can be better in practice (needs graph-search to be complete) Problem: branching factor is very large (need good heuristic: which actions may lead to goal?) Initial State ... ... DFS and loops: iterative deepening Loops ( s i " " s i ) in the search graph lead to inFnite branches in the search tree. The tree-search variant of D±S never terminates if it goes down an inFnite branch Remedy (iterative deepening): Try to Fnd solution of length l with D±S If this fails , l := l + # ; try again.
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 4
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 10

Lecture12HO - Classical Planning" CS440/ECE448: Intro...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online