Deeper model endgame analysis

A reference model of Fallible Endgame Play has been implemented and exercised with the chess-engine WILHELM. Past experiments have demonstrated the value of the model and the robustness of decisions based on it: experiments agree well with a Markov Model theory. Here, the reference model is exercised on the well-known endgame KBBKN

Reference fallible endgame play

A reference model of fallible endgame play is defined in terms of a spectrum of endgame players whose play ranges in competence from the optimal to the anti-optimal choice of move. They may be used as suitably skilled practice partners, to assess a player, to differentiate between otherwise equi-optimal moves, to promote or expedite a game result, to run Monte-Carlo simulations, and to identify the difficulty of a position or a whole endgame

Strategies for Constrained Optimisation

The latest 6-man chess endgame results confirm that there are many deep forced mates beyond the 50-move rule. Players with potential wins near this limit naturally want to avoid a claim for a draw: optimal play to current metrics does not guarantee feasible wins or maximise the chances of winning against fallible opposition. A new metric and further strategies are defined which support players’ aspirations and improve their prospects of securing wins in the context of a k-move rule

How To Be Better Prepared For A Paradigm Shift In Economic Theory, And Write Better Articles In The Meantime

The development of economic thought is not unlike the development of technological knowledge: paradigms can be discerned over time and across the field. Indeed, in its history economics has experienced paradigm shifts. There is no reason why it will not do so again in the future. In technology, as in economics, paradigms do not emerge from the blue, but build on precursors, possibly from fields other than our own discipline. Recognizing this draws our attention to other fields, preparing us for a possible paradigm shift. Understanding these other paradigms might best be done using historian Wight’s concepts of plot structure, myths, and cultural endowment. A better understanding of different paradigms allows us to combine ideas from other (sub-) fields with our own so that we are likely to come up with better ideas. In the meantime, as the parallel with the composition of music and the playing of chess shows, we compose better articles in the meantime because we are aware of the rules guiding our own compositions, yet. The history of our own field may be the first and best source for such inspiration

Collaborative computer personalities in the game of chess

Computer chess has played a crucial role in Artificial Intelligence research since the creation of the modem computer. It has gained this prominent position due to the large domain that it encompasses, including psychology, philosophy and computer science. The new and innovative techniques initially created for computer chess have often been successfully transferred to other divergent research areas such as theorem provers and economic models. The progress achieved by computers in the game of chess has been illustrated by Deep Blue’s famous victory over Garry Kasparov in 1997. However, further improvements are required if more complex problems are to be solved. In 1999 the Kasparov versus the World match took place over the Internet. The match allowed chess players from around the world to collaborate in a single game of chess against the then world champion, Garry Kasparov. The game was closely fought with Kasparov coming out on top. One of the most surprising aspects of the contest was the high quality of play achieved by the World team. The World team consisted of players with varying skill and style of play, despite this they achieved a level of play that was considered better than any of its individual members. The purpose of this research is to investigate if collaboration by different players can be successfully transferred to the domain of computer chess

A formal analysis of why heuristic functions work

AbstractMany optimization problems in computer science have been proven to be NP-hard, and it is unlikely that polynomial-time algorithms that solve these problems exist unless P=NP. Alternatively, they are solved using heuristics algorithms, which provide a sub-optimal solution that, hopefully, is arbitrarily close to the optimal. Such problems are found in a wide range of applications, including artificial intelligence, game theory, graph partitioning, database query optimization, etc. Consider a heuristic algorithm, A. Suppose that A could invoke one of two possible heuristic functions. The question of determining which heuristic function is superior, has typically demanded a yes/no answer—one which is often substantiated by empirical evidence. In this paper, by using Pattern Classification Techniques (PCT), we propose a formal, rigorous theoretical model that provides a stochastic answer to this problem. We prove that given a heuristic algorithm, A, that could utilize either of two heuristic functions H1 or H2 used to find the solution to a particular problem, if the accuracy of evaluating the cost of the optimal solution by using H1 is greater than the accuracy of evaluating the cost using H2, then H1 has a higher probability than H2 of leading to the optimal solution. This unproven conjecture has been the basis for designing numerous algorithms such as the A* algorithm, and its variants. Apart from formally proving the result, we also address the corresponding database query optimization problem that has been open for at least two decades. To validate our proofs, we report empirical results on database query optimization techniques involving a few well-known histogram estimation methods

17th Annual Student Academic Conference

Minnesota State University Moorhead Student Academic Conference abstract book.https://red.mnstate.edu/sac-book/1016/thumbnail.jp