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

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...

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Veröffentlicht in:Artificial intelligence Jg. 193; S. 129 - 148
Hauptverfasser: Ontañón, Santiago, Dellunde, Pilar, Godo, Lluís, Plaza, Enric
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier B.V 01.12.2012
Elsevier
Schlagworte:
Applied sciences
Argumentation
Artificial intelligence
Computer science; control theory; systems
Concept learning
Exact sciences and technology
Induction
Learning and adaptive systems
Logic
Machine learning
Concept learning
Argumentation
Logic
Induction
Machine learning
Bias
Preference theory
Inductive learning
Modeling
Circumscription
Default logic
Multiagent system
Revision
Supervised learning
Reasoning
Inductive logic programming
Artificial intelligence
ISSN:0004-3702, 1872-7921
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2012.08.006