GEQCA: Generic Qualitative Constraint Acquisition

Abstract

Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. On the one hand, the precise modelling of these constraints, which are formulated in various relation algebras, entails a number of possible logical combinations and requires expertise in constraint-based modelling. On the other hand, active constraint acquisition (CA) has been used successfully to support non-experienced users in learning conjunctive constraint networks through the generation of a sequence of queries. In this paper, we propose GEACQ, which stands for Generic Qualitative Constraint Acquisition, an active CA method that learns qualitative constraints via the concept of qualitative queries. GEACQ combines qualitative queries with time-bounded path consistency (PC) and background knowledge propagation to acquire the qualitative constraints of any scheduling or packing problem. We prove soundness, completeness and termination of GEACQ by exploiting the jointly exhaustive and pairwise disjoint property of qualitative calculus and we give an experimental evaluation that shows (i) the efficiency of our approach in learning temporal constraints and, (ii) the use of GEACQ on real scheduling instances.

What Are the Rules? Discovering Constraints from Data

“…However, we see computing the number of valid assignments for such models is even harder than for count constraints, and thus is a challenging problem. Ideally, we would extend our constraint language to the global constraint catalog (Beldiceanu, Carlsson, and Rampon 2012), which lists a large set of reusable constraints for constraint programming.…”

“…Learning constraints for constraint programming is a widely studied problem. Active learning approaches (Bessiere et al 2013;Tsouros and Stergiou 2020;Belaid et al 2022) derive constraints by asking queries in the form of partial or complete solutions and non-solutions. Even for simple problems, these approaches may require thousands of queries, which limits their applicability if a human must label these queries.…”

“…Existing approaches do not satisfactorily solve this task. Active learning (Bessiere et al 2013;Tsouros and Stergiou 2020;Belaid et al 2022) needs thousands of queries even for simple problems, which is intractable if a human expert must label these queries. Passive learning approaches (Pawlak and Krawiec 2017;Kumar et al 2020;Prestwich et al 2021) need invalid examples, i.e., non-solutions, in their training set.…”

Conversational Modeling for Constraint Satisfaction

“…We are beginning to see more consideration of volunteered information. QUACQ2 (Bessiere et al 2023) allows part of the problem to have already been supplied by the user and MGEQCA (Belaid et al 2022) allows for "background knowledge", including known constraints.…”

Active Disjunctive Constraint Acquisition

“…Based on querying an oracle that classifies samples as solutions and non-solutions, an active CA system automatically learns a constraint system that represents a concept the user has in mind. This is an active field of research, with many proposed extensions, for example allowing partial queries (Bessiere et al 2013;Bessiere et al 2020), incomplete answers (Tsouros, Stergiou, and Bessiere 2020), arguments (Shchekotykhin and Friedrich 2009), or acquisition of qualitative constraint networks (Belaid et al 2022).…”