Concurrent Reasoning with Inference Graphs

  • Daniel R. Schlegel
  • Stuart C. Shapiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8323)


Since their popularity began to rise in the mid-2000s there has been significant growth in the number of multi-core and multi-processor computers available. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs by treating propositional nodes as tasks which can be scheduled to operate upon messages sent between nodes via the arcs that already exist as part of the propositional graph representation. The use of scheduling heuristics within a prioritized message passing architecture allows inference graphs to perform very well in forward, backward, bi-directional, and focused reasoning. Tests demonstrate the usefulness of our scheduling heuristics, and show significant speedup in both best case and worst case inference scenarios as the number of processors increases.


Belief Revision Schedule Heuristic Inference Segment Inference Task Task Queue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel R. Schlegel
    • 1
  • Stuart C. Shapiro
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity at BuffaloBuffaloUSA

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