arXiv subjects

Raul Montoliu

Publications and source records attributed to Raul Montoliu.

3 recordsLinked to original sources

arXiv2026

On the RAID dataset of perceptual responses: analysis and statistical causes

This work analyzes the RAID dataset to evaluate human responses to affine image distortions, including rotation, translation, scaling, and Gaussian noise. Using Mean Squared Error (MSE), the study establishes human detection thresholds for these distortions, enabling comparison across types. Statistical analysis with ANOVA and Tukey Kramer tests reveals that observers are significantly more sensitive to Gaussian noise, which consistently produced the lowest detection thresholds. Fourier analysis further shows that high-frequency components act as a visual mask for Gaussian noise, demonstrating a strong correlation between high frequency energy and detection thresholds. Additionally, spectral orientation influences the perception of rotation. Finally, the study employs the PixelCNN model to show that image probability significantly correlates with detection thresholds for most distortions, suggesting that statistical likelihood affects human visual tolerance.

q-bio.NC↗

arXiv2020

Design and Implementation of TAG: A Tabletop Games Framework

This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of seven different tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research. The objective of this document is to serve as a central point where the framework can be described at length. TAG can be downloaded at: https://github.com/GAIGResearch/TabletopGames

cs.AI↗

arXiv2020

TotalBotWar: A New Pseudo Real-time Multi-action Game Challenge and Competition for AI

This paper presents TotalBotWar, a new pseudo real-time multi-action challenge for game AI, as well as some initial experiments that benchmark the framework with different agents. The game is based on the real-time battles of the popular TotalWar games series where players manage an army to defeat the opponent's one. In the proposed game, a turn consists of a set of orders to control the units. The number and specific orders that can be performed in a turn vary during the progression of the game. One interesting feature of the game is that if a particular unit does not receive an order in a turn, it will continue performing the action specified in a previous turn. The turn-wise branching factor becomes overwhelming for traditional algorithms and the partial observability of the game state makes the proposed game an interesting platform to test modern AI algorithms.

cs.AI↗