In this article, we describe a newly-invented chess variant called Switch-Side Chain-Chess that is demonstrably more challenging for humans and computers than the standard, international version of the game. A new rule states that players have the choice to switch sides with each other if a continuous link of pieces is created on the board. This simple rule increases significantly the complexity of chess, as perceived by the players, but not the actual size of its game tree. The new variant therefore more easily allows board game researchers to focus on the ‘higher level’ aspects of intelligence such as perception and intuition without being constrained by a larger search space as they would be if using a game like Go or Arimaa. They can also immediately build upon the tried and tested approaches already being used in strong chess engines instead of having to start from scratch or a lower level of progress as is the case with other games of this type.
We propose a class of chess variants, Multimove Chess (i,j), in which White
gets i moves per turn and Black gets j moves per turn. One side is said to win
when it takes the opponent's king. All other rules of chess apply. We prove
that if (i,j) is not (1,1) or (2,2), and if $i \geq \min(j,4)$, then White
always has a winning strategy, and otherwise Black always has a winning
In this paper we describe an approach to recognizing poorly textured ob- jects, that may contain holes and tubular parts, in cluttered scenes under ar- bitrary viewing conditions. To this end we develop a number of novel com- ponents. First, we introduce a new edge-based local feature detector that is invariant to similarity transformations. The features are localized on edges and a neighbourhood is estimated in a scale invariant manner. Second, the neighbourhood descriptor computed for foreground features is not affected by background clutter, even if the feature is on an object boundary. Third, the descriptor generalizes Lowe's SIFT method (12) to edges. An object model is learnt from a single training image. The object is then recognized in new images in a series of steps which apply progressively tighter geometric restrictions. A final contribution of this work is to allow sufficient flexibility in the geometric representation that objects in the same visual class can be recognized. Results are demonstrated for various object classes including bikes and rackets.
In this paper we present the context and results from a study, with 3rd to 6th grades children, about the relationship between chess and problem solving involving geometric and numeric patterns. The main result of this study is the existence of a relation between strength of play and patterns involving problem solving. We have included in the beginning an analysis of chess as a context for elementary mathematics problems, also showing its richness historically.
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.
Ches had its Deep Blue. "Jeopardy" had its Watson. Baseball has its sabermetrics, as chronicled in the hit book and film Moneyball. In each game, data mining has upended the field of play. And now the same big-league technologies are about to hit Scrabble.
Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors' performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a distributed environment. While pLoGANN proved to be designed efficiently, it failed to produce a competitive Lines of Action player, shedding light on the difficulty of developing a neural network to model such a large and complex solution space.
This paper is concerned with the problem of constructing a computing routine or “program” for a modern general purpose computer which will enable it to play chess. Although perhaps of no practical importance, the question is of theoretical interest, and it is hoped that a satisfactory solution of this problem will act as a wedge in attacking other problems of a similar nature and of greater significance. Some possibilities in this direction are:-
Machines for designing filters, equalizers, etc.
Machines for designing relay and switching circuits.
Machines which will handle routing of telephone calls based on the individual circumstances rather than by fixed patterns.
Machines for performing symbolic (non-numerical) mathematical operations.
Machines capable of translating from one language to another.
Machines for making strategic decisions in simplified military operations.
Machines capable of orchestrating a melody.
Machines capable of logical deduction.