Pervasive worlds are computing environments where a virtual world converges with the physical world through context-aware technologies such as sensors. In pervasive worlds, technology is distributed among entities that may be distributed geographically. We explore the concept, possibilities, and challenges of distributed pervasive worlds in a case study—an exergame entitled Running Othello. Com...

In 1993, mathematician Feinstein found out perfect play on 6×6 board Othello gives 16-20 loss for the first player by using computer. He reported on the Web that it took two weeks to search forty billion positions in order to obtain the result. In our previous papers, we confirmed the perfect play he found is correct. And we also found another perfect play different from the one he found to sea...

Bayesian learning, a statistical method, has been successfully applied to the feature combination stage of the evaluation function of the Othello program BILL, which won the 1989 North American Computer World Othello Championship. We have investigated the use of a novel neural network called HONEST in place of Bayesian learning in BILL. We implemented HONEST in six of BILL's twenty-six evaluati...

MTD(f) is a new minimax search algorithm, simpler and more efficient than previous algorithms. In tests with a number of tournament game playing programs for chess, checkers and Othello it performed better, on average, than NegaScout/PVS (the AlphaBeta variant used in practically all good chess, checkers, and Othello programs). One of the strongest chess programs of the moment, MIT's parallel c...

This article describes an application of three well{known statistical methods in the eld of game{tree search: using a large number of classi ed Othello positions, feature weights for evaluation functions with a game{phase{independent meaning are estimated by means of logistic regression, Fisher's linear discriminant, and the quadratic discriminant function for normally distributed features. The...

Many different approaches to game playing have been suggested including alpha-beta search, temporal difference learning, genetic algorithms, and coevolution. Here, a powerful new algorithm for neuroevolution, Neuro-Evolution for Augmenting Topologies (NEAT), is adapted to the game playing domain. Evolution and coevolution were used to try and develop neural networks capable of defeating an alph...