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Search, Games and Problem Solving

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Introduction to Artificial Intelligence

Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

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Abstract

Many AI problems, like automated theorem proving, game playing, planning or routing, involve combinatorial search in large discrete spaces. We introduce the classical uninformed and heuristic search algorithms such as for example A and apply them to simple examples. Game search techniques like minimax and alpha-beta pruning and their application in chess computers are discussed.

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Notes

  1. 1.

    The average branching factor of a tree is the branching factor that a tree with a constant branching factor, equal depth, and an equal amount of leaf nodes would have.

  2. 2.

    For an 8-puzzle the average branching factor depends on the starting state (see Exercise 6.2 on page 122).

  3. 3.

    When sorting in a new node from the node list, it may be advantageous to check whether the node is already available and, if so, to delete the duplicate.

  4. 4.

    Both graphs in Fig. 6.18 were generated by A. Batzill using the system described in [Bat16].

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Correspondence to Wolfgang Ertel .

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Ertel, W. (2017). Search, Games and Problem Solving. In: Introduction to Artificial Intelligence. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-58487-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-58487-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58486-7

  • Online ISBN: 978-3-319-58487-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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