The Communication of Inductive Inferences

W. H. E. Davies, Peter Edwards

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)
5 Downloads (Pure)

Abstract

We propose a new approach to communication between agents that perform inductive inference. Consider a community of agents where each agent has a limited view of the overall world. When an agent in this community induces a hypothesis about the world, it necessarily reflects that agent's partial view of the world. If an agent communicates a hypothesis to another agent, and that hypothesis is in conflict with the receiving agent's view of the world, then the receiving agent has to modify or discard the hypothesis. Previous systems have used voting methods or theory refinement techniques to integrate these partial hypotheses. However, these mechanisms risk destroying parts of the hypothesis that are correct. Our proposal is that an agent should communicate the bounds of an induced hypothesis, along with the hypothesis itself. These bounds allow the hypotheses to be judged in the context from which they were formed. This paper examines using version space boundary sets to represent these bounds. Version space boundary sets may be manipulated using set operations. These operations can be used to evaluate and integrate multiple partial hypotheses. We describe a simple implementation of this approach, and draw some conclusions on its practicality. Finally, we describe a tentative set of KQML operators for communicating hypotheses and their bounds.
Original languageEnglish
Title of host publicationDistributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments
EditorsG Weiss
PublisherSpringer-Verlag
Pages223-241
Volume1221
ISBN (Electronic)978-3-540-69050-4
ISBN (Print)978-3-540-62934-4
DOIs
Publication statusPublished - 1997

Keywords

  • multi-agent systems
  • learning
  • communication
  • induction

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    Davies, W. H. E., & Edwards, P. (1997). The Communication of Inductive Inferences. In G. Weiss (Ed.), Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments (Vol. 1221, pp. 223-241). Springer-Verlag. https://doi.org/10.1007/3-540-62934-3_51