1,740 research outputs found
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
Acquisition of Chess Knowledge in AlphaZero
What is learned by sophisticated neural network agents such as AlphaZero?
This question is of both scientific and practical interest. If the
representations of strong neural networks bear no resemblance to human
concepts, our ability to understand faithful explanations of their decisions
will be restricted, ultimately limiting what we can achieve with neural network
interpretability. In this work we provide evidence that human knowledge is
acquired by the AlphaZero neural network as it trains on the game of chess. By
probing for a broad range of human chess concepts we show when and where these
concepts are represented in the AlphaZero network. We also provide a
behavioural analysis focusing on opening play, including qualitative analysis
from chess Grandmaster Vladimir Kramnik. Finally, we carry out a preliminary
investigation looking at the low-level details of AlphaZero's representations,
and make the resulting behavioural and representational analyses available
online.Comment: 69 pages, 44 figure
Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data
Existing research has shown the potential of classifying Alzheimers Disease
(AD) from eye-tracking (ET) data with classifiers that rely on task-specific
engineered features. In this paper, we investigate whether we can improve on
existing results by using a Deep-Learning classifier trained end-to-end on raw
ET data. This classifier (VTNet) uses a GRU and a CNN in parallel to leverage
both visual (V) and temporal (T) representations of ET data and was previously
used to detect user confusion while processing visual displays. A main
challenge in applying VTNet to our target AD classification task is that the
available ET data sequences are much longer than those used in the previous
confusion detection task, pushing the limits of what is manageable by
LSTM-based models. We discuss how we address this challenge and show that VTNet
outperforms the state-of-the-art approaches in AD classification, providing
encouraging evidence on the generality of this model to make predictions from
ET data.Comment: ICMI 2023 long pape
Reinforcement learning approaches to the analysis of the emergence of goal-directed behaviour
Over recent decades, theoretical neuroscience, helped by computational methods
such as Reinforcement Learning (RL), has provided detailed descriptions of the
psychology and neurobiology of decision-making. RL has provided many insights
into the mechanisms underlying decision-making processes from neuronal to behavioral
levels. In this work, we attempt to demonstrate the effectiveness of RL
methods in explaining behavior in a normative setting through three main case
studies.
Evidence from literature shows that, apart from the commonly discussed cognitive
search process, that governs the solution procedure of a planning task, there
is an online perceptual process that directs the action selection towards moves that
appear more ‘natural’ at a given configuration of a task. These two processes can
be partially dissociated through developmental studies, with perceptual processes
apparently more dominant in the planning of younger children, prior to the maturation
of executive functions required for the control of search. Therefore, we
present a formalization of planning processes to account for perceptual features of
the task, and relate it to human data.
Although young children are able to demonstrate their preferences by using
physical actions, infants are restricted because of their as-yet-undeveloped motor
skills. Eye-tracking methods have been employed to tackle this difficulty. Exploring
different model-free RL algorithms and their possible cognitive realizations in
decision making, in a second case study, we demonstrate behavioral signatures of
decision making processes in eye-movement data and provide a potential framework
for integrating eye-movement patterns with behavioral patterns.
Finally, in a third project we examine how uncertainty in choices might guide exploration
in 10-year-olds, using an abstract RL-based mathematical model. Throughout,
aspects of action selection are seen as emerging from the RL computational
framework. We, thus, conclude that computational descriptions of the developing
decision making functions provide one plausible avenue by which to normatively characterize and define the functions that control action selection
Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision
The need for autonomous systems designed to play games, both strategy-based and
physical, comes from the quest to model human behaviour under tough and
competitive environments that require human skill at its best. In the last two decades,
and especially after the 1996 defeat of the world chess champion by a chess-playing
computer, physical games have been receiving greater attention. Robocup TM, i.e.
robotic football, is a well-known example, with the participation of thousands of
researchers all over the world. The robots created to play snooker/pool/billiards are
placed in this context. Snooker, as well as being a game of strategy, also requires
accurate physical manipulation skills from the player, and these two aspects qualify
snooker as a potential game for autonomous system development research. Although
research into playing strategy in snooker has made considerable progress using
various artificial intelligence methods, the physical manipulation part of the game is
not fully addressed by the robots created so far. This thesis looks at the different ball
manipulation options snooker players use, like the shots that impart spin to the ball in
order to accurately position the balls on the table, by trying to predict the ball
trajectories under the action of various dynamic phenomena, such as impacts.
A 3-degree of freedom robot, which can manipulate the snooker cue on a par with
humans, at high velocities, using a servomotor, and position the snooker cue on the
ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.
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