# Lecture13 - CS440/ECE448: Intro to Articial Intelligence!...

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Lecture 13: Review for midterm Prof. Julia Hockenmaier juliahmr@illinois.edu http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence

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Planning
Classical planning: assumptions The environment is fully observable, deterministic, static, known and Fnite. A plan is a linear sequence of actions ; Planning can be done off-line 3 CS440/ECE448: Intro AI

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Representations for planning: key questions How do we represent states ? What information do we need to know? What information can we (safely) ignore? How do we represent actions ? When can we perform an action? What changes when we perform an action? What stays the same? What level of detail do we care about?
Operators, actions and fuents Operator : carry(x) General knowledge of one kind of action: preconditions and effects Action : carry(BlockA) Ground instance of an operator Fluent : on(BlockA, BlockB, s) may be true in current state, but not after the action move(A,B,T) is performed.

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Operator name (and arity): move x from y to z move(x,y,z) Preconditions: when can the action be performed clear(x) ˭ clear(z) on(x,y) Effects : how does the world change? clear(y) on(x,z) clear(x) ¬clear(z) ¬on(x,y) => main differences between languages Representations for operators 6 CS440/ECE448: Intro AI new persist retract
Representations for states We want to know what state the world is in: What are the current properties of the entities? What are the current relations between the entities? Logic representation: Each state is a conjunction of ground predicates : Block(A) ˭ Block(B) Block(C) Table(T) On(A,B) On(B,T) On(C,T) Clear(A) Clear(C)

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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
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 9 CS440/ECE448: Intro AI

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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
Searching plan-space 1. Start with the empty plan = {start state, goal state} 2. Iteratively refne current plan to resolve Faws (refne = add new actions and constraints) ±law type 1: open goals (require more actions) ±law type 2: threats (require ordering constraints) 3. Solution = a plan without Faws 11 CS440/ECE448: Intro AI

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SATplan Represent a plan of Fxed length n as a ground formula in predicate logic. Translate this formula
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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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Lecture13 - CS440/ECE448: Intro to Articial Intelligence!...

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