Exact Learning of Qualitative Constraint Networks from Membership Queries

TL;DR

This paper introduces an algorithm for learning qualitative constraint networks (QCNs) from non-expert users via membership queries, utilizing constraint propagation and heuristics to reduce query complexity and improve efficiency.

Contribution

The paper presents a novel algorithm that efficiently learns QCNs from membership queries, incorporating constraint propagation and heuristics to minimize queries and enhance practical performance.

Findings

Constraint propagation reduces the number of queries needed.

Heuristics improve the learning efficiency.

Experimental results show promising performance on random instances.

Abstract

A Qualitative Constraint Network (QCN) is a constraint graph for representing problems under qualitative temporal and spatial relations, among others. More formally, a QCN includes a set of entities, and a list of qualitative constraints defining the possible scenarios between these entities. These latter constraints are expressed as disjunctions of binary relations capturing the (incomplete) knowledge between the involved entities. QCNs are very effective in representing a wide variety of real-world applications, including scheduling and planning, configuration and Geographic Information Systems (GIS). It is however challenging to elicit, from the user, the QCN representing a given problem. To overcome this difficulty in practice, we propose a new algorithm for learning, through membership queries, a QCN from a non expert. In this paper, membership queries are asked in order to elicit temporal or spatial relationships between pairs of temporal or spatial entities. In order to improve the time performance of our learning algorithm in practice, constraint propagation, through transitive closure, as well as ordering heuristics, are enforced. The goal here is to reduce the number of membership queries needed to reach the target QCN. In order to assess the practical effect of constraint propagation and ordering heuristics, we conducted several experiments on randomly generated temporal and spatial constraint network instances. The results of the experiments are very encouraging and promising.