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# Lecture13HO - CS440/ECE448 Intro to Articial Intelligence...

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Lecture 13: Review for midterm Prof. Julia Hockenmaier [email protected] http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to Artificial Intelligence Planning Classical planning: assumptions The environment is fully observable, deterministic, static, known and finite. A plan is a linear sequence of actions ; Planning can be done off-line 3 CS440/ECE448: Intro AI 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?

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Operators, actions and fluents 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. 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) Representations for planning Situation Calculus Strips Specify fluents Add -set Persist -set Specify fluents Add -set Delete -set By default fluents are deleted By default fluents persist
Planning algorithms State space search (DFS, BFS, etc.) Nodes = states; edges = actions; Heuristics (make search more efficient) Compute h() using relaxed version of the problem Plan space search (refinement of partial plans) Nodes = partial plans; edges: fix flaws 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 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 refine current plan to resolve flaws (refine = add new actions and constraints) Flaw type 1: open goals (require more actions) Flaw type 2: threats (require ordering constraints) 3. Solution = a plan without flaws 11 CS440/ECE448: Intro AI SATplan Represent a plan of fixed length n as a ground formula in predicate logic.

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Lecture13HO - CS440/ECE448 Intro to Articial Intelligence...

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