HEURISTIC SEARCH 4.0 Introduction 4.3 Using Heuristics I

HEURISTIC SEARCH 4.0 Introduction 4.3 Using Heuristics I

HEURISTIC SEARCH 4.0 Introduction 4.3 Using Heuristics I n Games 4.1

An Algorithm for Heuristic Search 4.4 Complexity Issues 4.2 Admissibility, Monotonicity, and

Informedness 4.5 Epilogue and References 4.6 Exercises

George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 1 The successive stages of open and closed that generate this graph are:

Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 2 Fig 4.16 State space generated in heuristic search of the 8-puzzle graph. Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 3

Fig 4.17 Open and closed as they appear after the 3rd iteration of heuristic search Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 4 Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005

5 Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 6 Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 7

Fig 4.18 Comparison of state space searched using heuristic search with space searched by breadth-first search. The proportion of the graph searched heuristically is shaded. The optimal search selection is in bold. Heuristic used is f(n) = g(n) + h(n) where h(n) is tiles out of place. Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 8

Fig 4.19 State space for a variant of nim. Each state partitions the seven matches into one or more piles. Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 9 Fig 4.20 Exhaustive minimax for the game of nim. Bold lines indicate forced win for MAX. Each node is marked with its derived value

(0 or 1) under minimax. Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 10 Fig 4.21 Minimax to a hypothetical state space. Leafstates show heuristic values; internal states show backed-up values.

Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 11 Fig 4.22 Heuristic measuring conflict applied to states of tic-tac-toe. Luger: Artificial Intelligence, 5th edition. Pearson Education Limited, 2005 12

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