Soar Checkers - An Intelligent Checkers Playing Agent in Soar

The classic board game of checkers is ideally suited for research in AI game-playing programs or agents. The objective behind Soar Checkers is to investigate if it is possible to create an agent-based game playing system that would beat novices with ease and at least challenge advanced novice to intermediate-level players by designing a rules-based expert system whose knowledge base consists of nothing more than the rules of checkers, and a set of guidelines for game-play based on good strategy. Soar was chosen as the platform for building this agent because it came built-in with features that facilitate creating rules-based expert systems, has been proven to be fairly reliable in developing such systems (including flight simulators) for over twenty years and makes it relatively straight-forward to have multiple agents play each other, and to add or modify features or strategies to the agents. Though the problem definition makes it inherently hard to objectively quantify the results, the objectives were successfully achieved for the most part. It was also seen that all other things being equal, the player going second ( White ) has a built-in advantage, thereby confirming a widely held belief among the checkers community

Learning to Play Othello with N-Tuple Systems

This paper investigates the use of n-tuple systems as position value functions for the game of Othello. The architecture is described, and then evaluated for use with temporal difference learning. Performance is compared with previously de-veloped weighted piece counters and multi-layer perceptrons. The n-tuple system is able to defeat the best performing of these after just five hundred games of self-play learning. The conclusion is that n-tuple networks learn faster and better than the other more conventional approaches

Fable: Finite automata based learning engine

The purpose of this thesis will be to demostrate the feasibility of building a system that is capable of automatically learning how to increase performance at a task that is presented to it by means of modeling the problem with a finite automation and the learning engine of FABLE. Current work in the field of automatic learning of FA\u27s (Finite Automation) is mostly focused on the building of the FA itself. This thesis will instead focus on the automated building of FA models to allow better decisions to be made in the future

A MATHEMATICAL ANALYSIS OF THE GAME OF CHESS

This paper analyzes chess through the lens of mathematics. Chess is a complex yet easy to understand game. Can mathematics be used to perfect a player’s skills? The work of Ernst Zermelo shows that one player should be able to force a win or force a draw. The work of Shannon and Hardy demonstrates the complexities of the game. Combinatorics, probability, and some chess puzzles are used to better understand the game. A computer program is used to test a hypothesis regarding chess strategy. Through the use of this program, we see that it is detrimental to be the first player to lose the queen. Ultimately, it is shown that mathematics exists inherently in chess. Therefore math can be used to improve, but not perfect, chess skills