Applying Alpha-beta Algorithm In A Chess Engine

Minimax Algorithm, is a solution to reduce the burden on hardware in chess engine. However, a more in-depth method is needed to further increase the search algorithm. One of those solutions is called Alpha-Beta Pruning algorithm. The idea is to eliminate the unnecessary nodes in the search tree

APPLYING ALPHA-BETA ALGORITHM IN A CHESS ENGINE

Minimax Algorithm, is a solution to reduce the burden on hardware in chess engine. However, a more in-depth method is needed to further increase the search algorithm. One of those solutions is called Alpha-Beta Pruning algorithm. The idea is to eliminate the unnecessary nodes in the search tree.

The Expected-Outcome Model of Two-Player Games

This paper introduces a new, crisp definition of two-player evaluation functions. These functions calculate a node's expected-outcome value. or the probability that a randomly chosen leaf beneath it will represent a win. The utility of these values to game programs will be assessed by a series of experiments that compare the performance of expected-outcome functions with that of some popular, previously studied evaluators. To help demonstrate the domain-independence of these new functions, the experiments will be run on variants of several games, including tic-tac-toe, Othello, and chess. In addition, the paper outlines a. new probabilistic model of game-trees which involves rethinking many long-accepted assumptions in light of the newly defined expected-outcome functions

Intelligence artificielle et optimisation avec parallélisme

This document is devoted to artificial intelligence and optimization. This part will bedevoted to having fun with high level ideas and to introduce the subject. Thereafter,Part II will be devoted to Monte-Carlo Tree Search, a recent great tool for sequentialdecision making; we will only briefly discuss other tools for sequential decision making;the complexity of sequential decision making will be reviewed. Then, part IIIwill discuss optimization, with a particular focus on robust optimization and especiallyevolutionary optimization. Part IV will present some machine learning tools, useful ineveryday life, such as supervised learning and active learning. A conclusion (part V)will come back to fun and to high level ideas.On parlera ici de Monte-Carlo Tree Search, d'UCT, d'algorithmes évolutionnaires et d'autres trucs et astuces d'IA;l'accent sera mis sur la parallélisation