Combining binary constraint networks in qualitative reasoning

  • Authors:

  • Jason Jingshi Li;Tomasz Kowalski;Jochen Renz;Sanjiang Li

  • Affiliations:

  • RSISE, The Australian National University, Canberra ACT 0200, Australia, email: jason.li|tomasz.kowalski|jochen.renz@anu.edu.au and NICTA Canberra Research Laboratory, Canberra ACT 2601, Australia;RSISE, The Australian National University, Canberra ACT 0200, Australia, email: jason.li|tomasz.kowalski|jochen.renz@anu.edu.au;RSISE, The Australian National University, Canberra ACT 0200, Australia, email: jason.li|tomasz.kowalski|jochen.renz@anu.edu.au and NICTA Canberra Research Laboratory, Canberra ACT 2601, Australia;State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P.R. China, email: lisanjiang@tsinghua.edu.cn

  • Venue:
  • Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
  • Year:
  • 2008

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Abstract

Constraint networks in qualitative spatial and temporal reasoning are always complete graphs. When one adds an extra element to a given network, previously unknown constraints are derived by intersections and compositions of other constraints, and this may introduce inconsistency to the overall network. Likewise, when combining two consistent networks that share a common part, the combined network may become inconsistent. In this paper, we analyse the problem of combining these binary constraint networks and develop certain conditions to ensure combining two networks will never introduce an inconsistency for a given spatial or temporal calculus. This enables us to maintain a consistent world-view while acquiring new information in relation with some part of it. In addition, our results enable us to prove other important properties of qualitative spatial and temporal calculi in areas such as representability and complexity.