Chess Endgames and Neural Networks

The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques

Expertise and intuition: A tale of three theories

Several authors have hailed intuition as one of the defining features of expertise. In particular, while disagreeing on almost anything that touches on human cognition and artificial intelligence, Hubert Dreyfus and Herbert Simon agreed on this point. However, the highly influential theories of intuition they proposed differed in major ways, especially with respect to the role given to search and as to whether intuition is holistic or analytic. Both theories suffer from empirical weaknesses. In this paper, we show how, with some additions, a recent theory of expert memory (the template theory) offers a coherent and wide-ranging explanation of intuition in expert behaviour. It is shown that the theory accounts for the key features of intuition: it explains the rapid onset of intuition and its perceptual nature, provides mechanisms for learning, incorporates processes showing how perception is linked to action and emotion, and how experts capture the entirety of a situation. In doing so, the new theory addresses the issues problematic for Dreyfus’s and Simon’s theories. Implications for research and practice are discussed

Temoral Difference Learning in Complex Domains

Submitted to the University of London for the Degree of Doctor of Philosophy in Computer Scienc

Temporal Difference Learning in Complex Domains

PhDThis thesis adapts and improves on the methods of TD(k) (Sutton 1988) that were successfully used for backgammon (Tesauro 1994) and applies them to other complex games that are less amenable to simple pattem-matching approaches. The games investigated are chess and shogi, both of which (unlike backgammon) require significant amounts of computational effort to be expended on search in order to achieve expert play. The improved methods are also tested in a non-game domain. In the chess domain, the adapted TD(k) method is shown to successfully learn the relative values of the pieces, and matches using these learnt piece values indicate that they perform at least as well as piece values widely quoted in elementary chess books. The adapted TD(X) method is also shown to work well in shogi, considered by many researchers to be the next challenge for computer game-playing, and for which there is no standardised set of piece values. An original method to automatically set and adjust the major control parameters used by TD(k) is presented. The main performance advantage comes from the learning rate adjustment, which is based on a new concept called temporal coherence. Experiments in both chess and a random-walk domain show that the temporal coherence algorithm produces both faster learning and more stable values than both human-chosen parameters and an earlier method for learning rate adjustment. The methods presented in this thesis allow programs to learn with as little input of external knowledge as possible, exploring the domain on their own rather than by being taught. Further experiments show that the method is capable of handling many hundreds of weights, and that it is not necessary to perform deep searches during the leaming phase in order to learn effective weight

Validation of machine-oriented strategies in chess endgames

This thesis is concerned with the validation of chess endgame strategies. It is also concerned with the synthesis of strategies that can be validated. A strategy for a given player is the specification of the move to be made by that player from any position that may occur. This move may be dependent on the previous moves of both sides. A strategy is said to be correct if following the strategy always leads to an outcome of at least the same game theoretic value as the starting position. We are not concerned with proving the correctness of programs that implement the strategies under consideration. We shall be working with knowledge-based programs which produce playing strategies, and assume that their concrete implementations (in POP2, PROLOG etc.) are correct. The synthesis approach taken attempts to use the large body of heuristic knowledge and theory, accumulated over the centuries by chessmasters, to find playing strategies. Our concern here is to produce structures for representing a chessmaster's knowledge wnich can be analysed within a game theoretic model. The validation approach taken is that a theory of the domain in the form of the game theoretic model of chess provides an objective measure of the strategy followed by a program. Our concern here is to analyse the structures created in the synthesis phase. This is an instance of a general problem, that of quantifying the performance of computing systems. In general to quantify the performance of a system we need,- A theory of the domain. - A specification of the problem to be solved. - Algorithms and/or domain-specific knowledge to be applied to solve the problem

Representation of Knowledge for Chess Endgames Towards a Self-Improving System

This thesis describes an investigation of the problems involved in representing knowledge within the task area of elementary Chess endgames. Two major criteria are taken for the choice of a model of & the chessplayer's knowledge : firstly, that algorithms constructed using the model should be natural from the viewpoint of a chessplayer and commensurate with his, view of the complexity of the task, and secondly that the algorithms should be capable of refinement in the light of experience in a manner which preserves the previous property. Elementary chess endgames are studied as a field in which programs based on tree-searching and traditional evaluation functions have achieved poor results and where tree-searching seems to play little or no part for people. It is therefore possible to examine problems of knowledge representation and program refinement largely independently of the tree-searching paradigm. A long term aim of the research is to develop a representation suitable as the basis for a fully automatic system of algorithm refinement, whilst maintaining the criteria given above. A model is proposed and algorithms are given for two endgames, King and Rook against King (KRK) and King and Pawn against King (KPK) using this model. It is argued that both algorithms are reasonably natural and compact representations and experiments in refining these algorithms are described in detail. In both cases, the process of refinement is shown to be a reasonably straightforward one (for people) and one which maintains the properties of naturalness and compactness. The possibility of automating this process is considered

Human and Machine Representations of Knowledge

Four ex1st1ng Knowledge-representations for the computat1on of s1m1lar functions 1n a chess endgame were 1mplemented on the same computer 1n the same language. They are compared w1th respect to effic1ency regard1ng time-space requirements. Three of these programs were then paraphrased 1nto English and all four were studied for their feasibility as 'open book' advice texts for the human beginner in chess. A formally verified set of rules was also tested for its suitability as an advice text. The possible effectiveness of these advice texts in 'closed book' form is considered. The above experiments comprise a case study of a phenomenon known as the "human window". This phenomenon mot1vated an analysis of four documented instances of mismatch between human and machine representations. These are: Three Mile Island II Air Traffic Control, III NORAD Mil1tary Computer System, IV The Hoogoven Royal Dutch Steel automation failur

Correct abstraction in counter-planning : a knowledge compilation approach

Knowledge compilation improves search-intensive problem-solvers that are easily specified but inefficient. One promising approach improves efficiency by constructing a database of problem-instance/best-action pairs that replace problem-solving search with efficient lookup. The database is constructed by reverse enumeration- expanding the complete search space backwards, from the terminal problem instances. This approach has been used successfully in counter-planning to construct perfect problem-solvers for sub domains of chess and checkers. However, the approach is limited to small problems because both the space needed to store the database and the time needed to generate the database grow exponentially with problem size. This thesis addresses these problems through two mechanisms. First, the space needed is reduced through an abstraction mechanism that is especially suited to counter-planning domains. The search space is abstracted by representing problem states as equivalence classes with respect to the goal achieved and the operators as equivalence classes with respect to how they influence the goals. Second, the time needed is reduced through a hueristic best-first control of the reverse enumeration. Since with larger problems it may be impractical to run the compiler to completion, the search is organized to optimize the tradeoff between the time spent compiling a domain and the coverage achieved over that domain. These two mechanisms are implemented in a system that has been applied to problems in chess and checkers. Empirical results demonstrate both the strengths and weaknesses of the approach. In most problems and 80/20 rule was demonstrated, where a small number of patterns were identified early that covered most of the domain, justifying the use of best-first search. In addition, the method was able to automatically generate a set of abstract rules that had previously required two person-months to hand engineer