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Article
Open AccessCombining data and theory for derivable scientific discovery with AI-Descartes
Scientists aim to discover meaningful formulae that accurately describe experimental data. Mathematical models of natural phenomena can be manually created from domain knowledge and fitted to data, or, in cont...
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Chapter and Conference Paper
Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning
Logical rules are a popular knowledge representation language in many domains. Recently, neural networks have been proposed to support the complex rule induction process. However, we argue that existing datase...
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Article
Open AccessVoting with random classifiers (VORACE): theoretical and experimental analysis
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific dom...
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Article
Multi-agent soft constraint aggregation via sequential voting: theoretical and experimental results
We consider scenarios where several agents must aggregate their preferences over a large set of candidates with a combinatorial structure. That is, each candidate is an element of the Cartesian product of the ...
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Chapter and Conference Paper
Updates and Uncertainty in CP-Nets
In this paper we present a two-fold generalization of conditional preference networks (CP-nets) that incorporates uncertainty. CP-nets are a formal tool to model qualitative conditional statements (cp-statemen...
