# Lect9 - Homework 2A due Thursday Planning Today part of...

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Homework 2A due Thursday Next Reinforcement Learning Begins statistical AI 1

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Classical Planning Using inference to find a sequence of operator instances (actions) that transform an initial state into a state in which the goal is satisfied. Initial State Goal Specification Operator Definitions Planner Solution Real World Applications: Scheduling, Logistics, Semantic web support, Computer gaming, 2
Planning vs. Search Interesting action sequences All action sequences  Search operators are “inferentially opaque” Planning allows reasoning about state features 3

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Domain Independent Planning Study the planning process Abstract Not domain dependent Ontology, operators, etc. define the domain Initial State Goal Specification Operator Definitions Planner Solution 4
Operators model world dynamics Situation Calculus Strips Operators PDDL Operators* Search State Space: Forward / Backward Plan Space Heuristics Propositionalization * Ch10 Pure FOPC Specialized syntax 5

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All Reachable Situations are Defined Given: 1) the Initial State 2) Axioms of World Change (operator definitions)   Initial State Operator Definitions Planning is theorem proving Find a situation where the goal holds Si Si,a2 Si,a17 Si,a2,a34 Si,a4 Si,a15 Si,a15a4 Si,a4,a3 6
Blocks World A B C G F E D Several ontologies possible (ways to conceptualize the world and its changes) Operator - General knowledge of one kind of change Action - Ground instance of an operator Silly domain but concisely illustrates many GENERAL planning issues 7

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change a block’s position differently Move-Block Move-Gripper Grasp-Block Move-Gripper UnGrasp-Block Move-Gripper Open-Gripper Move-Gripper Close-Gripper ... Motor1-Velocity
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Lect9 - Homework 2A due Thursday Planning Today part of...

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