Matching Qualitative Constraint Networks with Online Reinforcement Learning

Malumbo Chipofya

Forschungsartikel in Sammelband (Konferenz) | Peer reviewed

Zusammenfassung

Local Compatibility Matrices (LCMs) are mechanisms for computing heuristics for graph matching that are particularly suited for matching qualitative constraint networks enabling the transfer of qualitative spatial knowledge between qualitative reasoning systems or agents. A system of LCMs can be used during matching to compute a pre-move evaluation, which acts as a prior optimistic estimate of the value of matching a pair of nodes, and a post-move evaluation which adjusts the prior estimate in the direction of the true value upon completing the move. We present a metaheuristic method that uses reinforcement learning to improve the prior estimates based on the posterior evaluation. The learned values implicitly identify unprofitable regions of the search space. We also present data structures that allow a more compact implementation, limiting the space and time complexity of our algorithm.

Details zur Publikation

Herausgeber*innenBenzmüller Christoph, Sutcliffe Geoff, Rojas Raul
Seitenbereich266-279
VerlagEasyChair
Titel der ReiheEPiC Series in Computing (ISSN: 2398-7340)
Nr. in Reihe41
StatusVeröffentlicht
Veröffentlichungsjahr2016
Sprache, in der die Publikation verfasst istEnglisch
KonferenzGCAI 2016. 2nd Global Conference on Artificial Intelligence, Berlin, Germany, undefined
Link zum Volltexthttp://www.easychair.org/publications/download/Matching_Qualitative_Constraint_Networks_with_Online_Reinforcement_Learning
Stichwörterqualitative constraint network; qcn matching; local compatibility matrix; reinforcement learning; metaheuristic; sarsa

Autor*innen der Universität Münster

Chipofya, Malumbo Chaka
Professur für Geoinformatik (Prof. Schwering) (SIL)