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Lecture 3: State-spaces and Uninformed Search

Overview. Intelligent agents: problem solving as search State-space problem formulation consists ofstate spaceoperators, successor functionstart stategoal statesCost functionThe search graph A Search Tree is an efficient way to represent the search process There are a variety of search al

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Lecture 3: State-spaces and Uninformed Search

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    1. Lecture 3: State-spaces and Uninformed Search ICS 271 Fall 2008

    2. Overview Intelligent agents: problem solving as search State-space problem formulation consists of state space operators, successor function start state goal states Cost function The search graph A Search Tree is an efficient way to represent the search process There are a variety of search algorithms, including Depth-First Search Breadth-First Search Others which use heuristic knowledge (in future lectures)

    3. Problem-Solving Agents Intelligent agents can solve problems by searching a state-space State-space Model 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 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 Operators operators are legal actions which the agent can take to move from one state to another

    4. The Traveling Salesperson Problem Find the shortest tour that visits all cities without visiting any city twice and return to starting point. State: sequence of cities visited S0 = A

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