GAMES: A new Scenario for Software and Knowledge Reuse

Games are a well-known test bed for testing search algorithms and learning methods, and many authors have presented numerous reasons for the research in this area. Nevertheless, they have not received the attention they deserve as software projects. In this paper, we analyze the applicability of software and knowledge reuse in the games domain. In spite of the need to find a good evaluation function, search algorithms and interface design can be said to be the primary concerns. In addition, we will discuss the current state of the main statistical learning methods and how they can be addressed from a software engineering point of view. So, this paper proposes a reliable environment and adequate tools, necessary in order to achieve high levels of reuse in the games domain

Chess players' thinking revisited

The main result of De Groot’s ([1946] 1978) classical study of chessplayers’ thinking was that players of various levels of skill do not differ in the macrostructure of their thought process (in particular with respect to the depth of search and to the number of nodes investigated). Recently, Holding (1985, 1992) challenged these results and proposed that there are skill differences in the way players explore the problem space. The present study replicates De Groot’s (1978) problem solving experiment. Results show that Masters differ from weak players in more ways than found in the original study. Some of the differences support search models of chess thinking, and others pattern recognition models. The theoretical discussion suggests that the usual distinction between search and pattern recognition models of chess thinking is unwarranted, and proposes a way of reconciling the two approaches

Integrating Learning from Examples into the Search for Diagnostic Policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on todays desktop computers

A Survey of Monte Carlo Tree Search Methods

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

Intuition in chess: a study with world-class players

Intuition plays a central role in cognition in general and expertise in particular. Dreyfus and Dreyfus’s (1986) and Gobet and Chassy’s (2008) theories of expert intuition propose that a characteristic feature of expert intuition is the holistic understanding displayed by experts. The ideal way to test this prediction is to use highly expert participants and short presentation times. Chess players (N = 63), ranging from candidate masters to world-class players, had to evaluate chess problems. Evaluating the problems required an understanding of the position as a whole. Results demonstrated an effect of skill (better players had better evaluations), complexity (simpler positions were better evaluated than complex positions) and balance (accuracy diminished when the true evaluations became more extreme). A regression analysis showed that skill accounted for 44% of the variance in evaluation error. These important results support the central role of holistic intuition in expertise

Quoridor agent using Monte Carlo Tree Search

This thesis presents a preliminary study using Monte Carlo Tree Search (MCTS) upon the board game of Quoridor. The system is shown to perform well against current existing methods, defeating a set of player agents drawn from an existing digital implementation

Deep learning investigation for chess player attention prediction using eye-tracking and game data

This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces. We have used scan path and fixation data from players engaged in solving chess problems, to compute 6600 saliency maps associated to the corresponding chess piece configurations. This corpus is completed with synthetically generated data from actual games gathered from an online chess platform. Experiments realized using both scan-paths from chess players and the CAT2000 saliency dataset of natural images, highlights several results. Deep features, pretrained on natural images, were found to be helpful in training visual attention prediction for chess. The proposed neural network architecture is able to generate meaningful saliency maps on unseen chess configurations with good scores on standard metrics. This work provides a baseline for future work on visual attention prediction in similar contexts

Response Time Distributions in Rapid Chess: A Large-Scale Decision Making Experiment

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation