28,832 research outputs found
Learning to Play Chess from Textbooks (LEAP): a Corpus for Evaluating Chess Moves based on Sentiment Analysis
Learning chess strategies has been investigated widely, with most studies
focussing on learning from previous games using search algorithms. Chess
textbooks encapsulate grandmaster knowledge, explain playing strategies and
require a smaller search space compared to traditional chess agents. This paper
examines chess textbooks as a new knowledge source for enabling machines to
learn how to play chess -- a resource that has not been explored previously. We
developed the LEAP corpus, a first and new heterogeneous dataset with
structured (chess move notations and board states) and unstructured data
(textual descriptions) collected from a chess textbook containing 1164
sentences discussing strategic moves from 91 games. We firstly labelled the
sentences based on their relevance, i.e., whether they are discussing a move.
Each relevant sentence was then labelled according to its sentiment towards the
described move. We performed empirical experiments that assess the performance
of various transformer-based baseline models for sentiment analysis. Our
results demonstrate the feasibility of employing transformer-based sentiment
analysis models for evaluating chess moves, with the best performing model
obtaining a weighted micro F_1 score of 68%. Finally, we synthesised the LEAP
corpus to create a larger dataset, which can be used as a solution to the
limited textual resource in the chess domain.Comment: 27 pages, 10 Figures, 9 Tabel
Learning to Play Chess from Textbooks (LEAP):a Corpus for Evaluating Chess Moves based on Sentiment Analysis
Learning chess strategies has been investigated widely, with most studies focussing on learning from previous games using search algorithms. Chess textbooks encapsulate grandmaster knowledge, explain playing strategies and require a smaller search space compared to traditional chess agents. This paper examines chess textbooks as a new knowledge source for enabling machines to learn how to play chess -- a resource that has not been explored previously. We developed the LEAP corpus, a first and new heterogeneous dataset with structured (chess move notations and board states) and unstructured data (textual descriptions) collected from a chess textbook containing 1164 sentences discussing strategic moves from 91 games. We firstly labelled the sentences based on their relevance, i.e., whether they are discussing a move. Each relevant sentence was then labelled according to its sentiment towards the described move. We performed empirical experiments that assess the performance of various transformer-based baseline models for sentiment analysis. Our results demonstrate the feasibility of employing transformer-based sentiment analysis models for evaluating chess moves, with the best performing model obtaining a weighted micro F_1 score of 68%. Finally, we synthesised the LEAP corpus to create a larger dataset, which can be used as a solution to the limited textual resource in the chess domain
Chicken or Checkin'? Rational Learning in Repeated Chess Games
We examine rational learning among expert chess players and how they update their beliefs in repeated games with the same opponent. We present a model that explains how equilibrium play is affected when players change their choice of strategy when receiving additional information from each encounter. We employ a large international panel dataset with controls for risk preferences and playing skills whereby the latter accounts for ability. Although expert chess players are intelligent, productive and equipped with adequate data and specialized computer programs, we find large learning effects. Moreover, as predicted by the model, risk-averse players learn substantially faster.risk aversion, rational learning, beliefs
Specialization effect and its influence on memory and problem solving in expert chess players
Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise – players remembering positions and solving problems from their area of specialization performed at around the level of players one standard deviation above them in general skill. Their problem solving strategy also changed depending on whether the problem was within their area of specialization or not. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context independent general purpose problem solving strategies to teach to future experts is unlikely to be successful
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
Playing Smart - Artificial Intelligence in Computer Games
Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games
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