A defeasible reasoning model of inductive concept learning from examples and communication

  • Authors:

  • Santiago OntañóN;Pilar Dellunde;LluíS Godo;Enric Plaza

  • Affiliations:

  • IIIA-CSIC, Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Campus UAB, 08193 Bellaterra, Catalonia, Spain and Computer Science Department, Drexel University, P ...;IIIA-CSIC, Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Campus UAB, 08193 Bellaterra, Catalonia, Spain and Universitat Autònoma de Barcelona, 08193 Bel ...;IIIA-CSIC, Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Campus UAB, 08193 Bellaterra, Catalonia, Spain;IIIA-CSIC, Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Campus UAB, 08193 Bellaterra, Catalonia, Spain

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2012

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Abstract

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they correspond to a rather well-behaved non-monotonic logic. We will also show that with the addition of a preference relation on inductive theories we can characterize the inductive bias of ICL algorithms. The second part of the paper shows how this logical characterization of inductive generalization can be integrated with another form of non-monotonic reasoning (argumentation), to define a model of multiagent ICL. This integration allows two or more agents to learn, in a consistent way, both from induction and from arguments used in the communication between them. We show that the inductive theories achieved by multiagent induction plus argumentation are sound, i.e. they are precisely the same as the inductive theories built by a single agent with all data.