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

Improved Reinforcement Learning with Curriculum

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions which lead to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state - we call this an end-game-first curriculum. Currently the state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum; instead learning from the entire game at all times. By employing an end-game-first training curriculum to train an AlphaZero inspired player, we empirically show that the rate of learning of an artificial player can be improved during the early stages of training when compared to a player not using a training curriculum.Comment: Draft prior to submission to IEEE Trans on Games. Changed paper slightl

Developing reproducible and comprehensible computational models

Quantitative predictions for complex scientific theories are often obtained by running simulations on computational models. In order for a theory to meet with wide-spread acceptance, it is important that the model be reproducible and comprehensible by independent researchers. However, the complexity of computational models can make the task of replication all but impossible. Previous authors have suggested that computer models should be developed using high-level specification languages or large amounts of documentation. We argue that neither suggestion is sufficient, as each deals with the prescriptive definition of the model, and does not aid in generalising the use of the model to new contexts. Instead, we argue that a computational model should be released as three components: (a) a well-documented implementation; (b) a set of tests illustrating each of the key processes within the model; and (c) a set of canonical results, for reproducing the model’s predictions in important experiments. The included tests and experiments would provide the concrete exemplars required for easier comprehension of the model, as well as a confirmation that independent implementations and later versions reproduce the theory’s canonical results

Templates in chess memory: A mechanism for recalling several boards

This paper addresses empirically and theoretically a question derived from the chunking theory of memory (Chase & Simon, 1973): To what extent is skilled chess memory limited by the size of short-term memory (about 7 chunks)? This question is addressed first with an experiment where subjects, ranking from class A players to grandmasters, are asked to recall up to 5 positions presented during 5 seconds each. Results show a decline of percentage of recall with additional boards, but also show that expert players recall more pieces than is predicted by the chunking theory in its original form. A second experiment shows that longer latencies between the presentation of boards facilitate recall. In a third experiment, a Chessmaster gradually increases the number of boards he can reproduce with higher than 70% average accuracy to nine, replacing as many as 160 pieces correctly. To account for the results of these experiments, a revision of the Chase-Simon theory is proposed. It is suggested that chess players, like experts in other recall tasks, use long-term memory retrieval structures (Chase & Ericsson, 1982) or templates in addition to chunks in STM, to store information rapidly

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