'Max node' presentation slideshows

Cognitive Game Theory

Cognitive Game Theory. Alpha-Beta minimax search Inductive Adversary Modeling Evolutionary Chess. Jennifer Novosad, Justin Fox and Jeremie Pouly. Motivation. Good benchmark Similar to military or financial domains Computer can beat humans Fun $. Reasoning Techniques for Games.

1.06k views • 85 slides

Adversarial Search Aka Games

Adversarial Search Aka Games. Chapter 6. Some material adopted from notes by Charles R. Dyer, University of Wisconsin-Madison. Overview. Game playing State of the art and resources Framework Game trees Minimax Alpha-beta pruning Adding randomness. Why study games?.

958 views • 84 slides

Adversarial Search Aka Games

Adversarial Search Aka Games. Chapter 6. Some material adopted from notes by Charles R. Dyer, University of Wisconsin-Madison. Overview. Game playing State of the art and resources Framework Game trees Minimax Alpha-beta pruning Adding randomness. Why study games?.

1k views • 80 slides

Midterm Review

Midterm Review. Dr. Bernard Chen Ph.D. University of Central Arkansas Spring 2011. Outline. Ch3 Structures and Strategies for State Space Search Ch4 Heuristic Search Ch5 Stochastic Search. Introduction to Representation .

875 views • 59 slides

Minimax with Alpha Beta Pruning

Minimax with Alpha Beta Pruning. The minimax algorithm is a way of finding an optimal move in a two player game. Alpha-beta pruning is a way of finding the optimal minimax solution while avoiding searching subtrees of moves which won't be selected.

2.3k views • 48 slides

Course: Engineering Artificial Intelligence

620 views • 46 slides

Game Playing

663 views • 42 slides

Game Playing

553 views • 42 slides

Game Playing

541 views • 42 slides

Chapter 6 : Game Search ?? ??

Chapter 6 : Game Search ?? ??. ?? ??? ??. - Exhaustive search is almost impossible. ==> mostly too many branching factor and too many depths. (e.g., ?? : (18 * 18)! ), ?? :DeepBlue ? - ?????? (Static evaluation score) ==> board quality - maximizing player ==> hoping to win (me)

434 views • 36 slides

Iterative Deepening A*

Iterative Deepening A*. Algorithm A* has memory demands that increase exponentially with the depth of the goal node (unless our estimations are perfect). You remember that we improved the space efficiency of the breadth-first search algorithm by applying iterative deepening .

680 views • 32 slides

Notes 6: Game-Playing

Notes 6: Game-Playing. ICS 271 Fall 2006. Overview. Computer programs which play 2-player games game-playing as search with the complication of an opponent General principles of game-playing and search evaluation functions minimax principle alpha-beta-pruning heuristic techniques

478 views • 31 slides

ITCS 3153 Artificial Intelligence

408 views • 30 slides

Chapter 12 Adversarial Search

398 views • 28 slides

441 views • 27 slides

CMSC 463

410 views • 26 slides

CMSC 671 Fall 2001

333 views • 24 slides

Min-Max Heaps

Min-Max Heaps. A double-ended priority queue is a data structure that supports the following operations: inserting an element with an arbitrary key deleting an element with the largest key deleting an element with the smallest key A Min-Max Heap supports all of the above operations.

636 views • 22 slides

Adversarial Search

Adversarial Search. In this lecture, we introduce a new search scenario: game playing two players, zero-sum game, (win-lose, lose-win, draw) perfect accessibility to game information (unlike bridge) 4. Deterministic rule (no dice used, unlike Back-gammon).

363 views • 10 slides

Probability

244 views • 10 slides