CS561Lecture3

CS561Lecture3 - Artifcial Intelligence Lectures 3-4 (oF...

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Unformatted text preview: Artifcial Intelligence Lectures 3-4 (oF 3-5): Problem Solving and Search (Part II, Chapter 3) Spring 2010 Instructor: Paul S. Rosenbloom 2 This 1.5 Week’s Lectures Problem solving Search Blind search Search strategies With repeated states Knowledge in Search How can knowledge help search? Best-first search and its variants More on heuristic functions 3 Problem Solving in AI Fundamentally about achieving a goal Being rich Winning a game of chess Getting to a particular location Saving people from a burning building Getting an A in this course May take on a variety of variant formulations Find any state that matches goal Find all states that match goal Prove that no state matches goal Find best state that matches goal Find best path to a goal state 4 Core Activity Determine Action to Perform Next May have knowledge that tells you what to do This is the predominant mode for human experts They directly “see” what to do next But will look at knowledge later in course Can search over sequences of actions to find one that achieves goal and then pick first action in sequence Search is fundamentally a response to a lack of knowledge But it itself requires knowledge in the form of a world model Could search in world instead of in a model, but this essentially involves making a random next move Generally referred to as exploration or online search Can lead to learning a world model for later use in internal search 5 Problem Formulation Given goal, first step is to formulate the problem for search First Step: Specify an appropriate world model as a problem space comprising A set of states representing situations A set of actions which move among states Then will need to instantiate problem in space 6 Route Finding in Romania Traveling Salesman Problem is one variation where must visit each city exactly once and want lowest cost path 7 Specifying States Actions must be specified explicitly, but states may be specified Explicitly as a list Implicitly as a cross-product of features UB: [((B, W) × (P, R, N, B, K, Q)) + Blank] Sq = 13 64 (~10 71 ) Implicitly via an initial state and a set of actions … … 8 Instantiating Problem in Space Given problem space, second step is to instantiate problem in space Current/initial state representing starting state for problem solving Goal state(s) representing situations that meet goal Can be explicit as a list of one or more states Can be implicit as a binary test on states Checkmate: King under attack and with no ability to move to a safe square, block attack or take attacker 9 Eight Puzzle Initial State: A configuration of Tiles Goal State: Another configuration Operators: Slide a tile into space Or “move” space Can actually only reach half of all configurations Search spaces get dramatically larger as get to fifteen puzzle, twenty four puzzle, etc. 10...
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This note was uploaded on 03/05/2010 for the course CS 561 taught by Professor Moradi during the Spring '09 term at USC.

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CS561Lecture3 - Artifcial Intelligence Lectures 3-4 (oF...

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