Journal Articles Journal of Artificial Intelligence Research Year : 2024

Query-driven Qualitative Constraint Acquisition

Nassim Belmecheri
Arnaud Gotlieb
Nadjib Lazaar
Helge Spieker

Abstract

Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.
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lirmm-04835853 , version 1 (13-12-2024)

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Mohamed-Bachir Belaid, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker. Query-driven Qualitative Constraint Acquisition. Journal of Artificial Intelligence Research, 2024, 79, pp.241-271. ⟨10.1613/jair.1.14752⟩. ⟨lirmm-04835853⟩
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