# Lecture12 - CS440/ECE448: Intro to Articial Intelligence!...

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Lecture 12: Planning algorithms Prof. Julia Hockenmaier juliahmr@illinois.edu http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence

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State transition system Σ = (S,A, γ ) 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 Fuents Add -set Persist -set Specify Fuents Add -set Delete -set By default Fuents are deleted By default Fuents persist

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Sussman anomaly B A C Start C B A Goal Start: On(C,A) Goal: On(A,B) ˭ On(B,C)
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)

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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)

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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
State space search

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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 ... ...

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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 + Δ ; try again.
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## This note was uploaded on 10/13/2011 for the course CS 440 taught by Professor Levinson,s during the Spring '08 term at University of Illinois, Urbana Champaign.

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Lecture12 - CS440/ECE448: Intro to Articial Intelligence!...

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