Multiagent Inductive Learning is the problem that groups of agents face when they want to perform inductive learning, but the data of interest is distributed among them. This paper focuses on concept learning, and presents A-MAIL, a framework for multiagent induction integrating ideas from inductive learning, case-based reasoning and argumentation. Argumentation is used as a communication frame...

This paper demonstrates how A-Prolog can be used to solve the problem of non-monotonic inductive learning in the context of the learning of the behavior of dynamic domains. Non-monotonic inductive learning is an extension of traditional inductive learning, characterized by the use of default negation in the background knowledge and/or in the clauses being learned. The importance of non-monotoni...

Inductive learning is the method of learning from observations. Inductive learning has important applications over a wide range of area including pattern recognition, language acquisition, bio-informatics and intelligent agent design. Because of such diverse applicability, inductive learning methods including automata learning, grammar induction, hidden markov model learning and symbolic statis...

We applied inductive learning to a problem, engineering design optimization, for which the applicability of inductive learning is not immediately obvious. In this paper we describe how we were able to formulate two pieces of the optimization problem as inductive learning problems, and we describe some of the lessons that we learned in the process.

ion Refinement via Inductive Learning Alexey Loginov, Thomas Reps, and Mooly Sagiv 1 Comp. Sci. Dept., University of Wisconsin; {alexey,reps}@cs.wisc.edu 2 School of Comp. Sci., Tel-Aviv University; [email protected] Abstract. This paper concerns how to automatically create abstractions for program analysis. We show that inductive learning, the goal of which is to identify general rules fro...

Inductive reasoning is one of the important characteristics of human intelligence. Researchers have regarded inductive reasoning as one of the seven primary mental abilities that are accounted for intelligent behaviours; researches also showed that it is the best predictor for academic performance. Despite its recognised importance underlying the learning process of human beings, little effort ...

The intrinsic inability of artificial neural networks to generalize from examples, i.e., to leam inductively, is exemplified based on several very simple requirements for an inductive learning machine. Keyw0rds:Neural Networks, Generalization, Inductive Learning, Learning Machines, Pattem Recognition