Lecture 17-Planning

Lecture 17-Planning - CS 561 Artificial Intelligence...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, [email protected] TAs: Nadeesha Ranashinghe ( [email protected] ) William Yeoh ( [email protected] ) Harris Chiu ( [email protected] ) Lectures: MW 5:00-6:20pm, OHE 122 / DEN Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Spring2010/ This class will use http://www.uscden.net/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (2nd ed)
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Planning (AIMA Ch. 11) Search vs. planning STRIPS operators Partial-order planning 2 CS561 - Lecture 17 - Macskassy - Spring 2010
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What we have so far Can TELL KB about new percepts about the world KB maintains model of the current world state Can ASK KB about any fact that can be inferred from KB How can we use these components to build a planning agent ? i.e., an agent that constructs plans that can achieve its goals, and that then executes these plans? 3 CS561 - Lecture 17 - Macskassy - Spring 2010
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Remember: Problem-Solving Agent function S IMPLE -P ROBLEM -S OLVING -A GENT ( percept ) returns an action static: seq , an action sequence, initially empty state , some description of the current world state goal , a goal, initially null problem , a problem formulation state U PDATE -S TATE ( state , percept ) if seq is empty then goal F ORMULATE -G OAL ( state ) problem F ORMULATE -P ROBLEM ( state , goal ) seq S EARCH ( problem ) action R ECOMMENDATION ( seq , state ) seq R EMAINDER ( seq , state ) return action Note: This is offline problem-solving. Onlineproblem-solving involves acting w/o complete knowledge of the problem and environment // What is the current state? // From LA to San Diego (given curr. state) // e.g., Gas usage // If fails to reach goal, update 4 CS561 - Lecture 17 - Macskassy - Spring 2010
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Simple planning agent Use percepts to build model of current world state IDEAL-PLANNER: Given a goal, algorithm generates plan of action STATE-DESCRIPTION: given percept, return initial state description in format required by planner MAKE-GOAL-QUERY: used to ask KB what next goal should be 5 CS561 - Lecture 17 - Macskassy - Spring 2010
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A Simple Planning Agent function SIMPLE-PLANNING-AGENT(percept) returns an action static : KB, a knowledge base (includes action descriptions) p, a plan (initially, NoPlan) t, a time counter (initially 0) local variables :G, a goal current, a current state description TELL(KB, MAKE-PERCEPT-SENTENCE(percept, t)) current STATE-DESCRIPTION(KB, t) if p = NoPlan then G ASK(KB, MAKE-GOAL-QUERY(t)) p IDEAL-PLANNER(current, G, KB) if p = NoPlan or p is empty then action NoOp else action FIRST(p) p REST(p) TELL(KB, MAKE-ACTION-SENTENCE(action, t)) t t+1 return action Like popping from a stack 6 CS561 - Lecture 17 - Macskassy - Spring 2010
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Search vs. planning 7 CS561 - Lecture 17 - Macskassy - Spring 2010
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Search vs. planning 8 CS561 - Lecture 17 - Macskassy - Spring 2010
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Planning in situation calculus 9 CS561 - Lecture 17 - Macskassy - Spring 2010
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Lecture 17-Planning - CS 561 Artificial Intelligence...

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