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lecture2 ch3

Artificial Intelligence: A Modern Approach

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1 ICS-171:Lecture 2: 1 Lecture 2: Problem Solving as Search; Uniformed Search ICS 171, Summer 2000 ICS-171:Lecture 2: 2 Outline Representing problems as search state space operators start state goal states A Search Tree is an efficient way to represent how a search algorithm explores the state space There are a variety of specific search techniques, including Depth-First Search Breadth-First Search
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2 ICS-171:Lecture 2: 3 What do these problems have in common? Find the layout of chips on a circuit board which minimize the total length of interconnecting wires Schedule which airplanes and crew fly to which cities for American, United, British Airways, etc Write a program which can play chess against a human Build a system which can find human faces in an arbitrary digital image Program a tablet-driven portable computer to recognize your handwriting Decrypt data which has been encrypted but you do not have the key Answer they can all be formulated as search problems ICS-171:Lecture 2: 4 Problem-Solving Agents Intelligent agents can solve problems by searching a state-space State space the agent’s model of the world usually a set of discrete states e.g., in driving, the states in the model could be towns/cities Goal State(s) a goal is defined as a desirable state for an agent For now: all goal states have utility 1, and all non-goals have utility 0 there may be many states which satisfy the goal • e.g., drive to a town with a ski-resort or just one state which satisfies the goal • e.g., drive to Mammoth Mountain Operators operators are legal actions which the agent can take to move from one state to another
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3 ICS-171:Lecture 2: 5 State Spaces and Search A State-Space Representation for Search Problems search = “journey” through a set of states start at initial state S want to get to a goal state G (utility of these states = 1) nodes represent states links represent state-transitions, may have associated costs A search algorithm specifies precisely how to explore the state space to: find any path to G find all paths to G find the lowest cost path to G We will focus on • finding any path to any goal state G • ignore path costs for now ICS-171:Lecture 2: 6 Defining Search Problems A statement of a Search problem has 4 components 1. A set of states 2. A set of “operators” which allow one to get from one state to another 3. A start state S 4. A set of possible goal states, or ways to test for goal states Search solution consists of a unique goal state G a sequence of operators which transform S into a goal state G (this is the sequence of actions the agent would take to maximize the success function) For now we are interested in any path from S to G Representing real problems in a search framework may be many ways to represent states and operators key idea: represent only the relevant aspects of the problem
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lecture2 ch3 - Lecture 2 Problem Solving as Search...

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