A connectionist approach to learning legal moves in Tower-of-Hanoi

Andrew Sohn, Jean Luc Gaudiot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

While optimizing scheduling problems such as the traveling salesman problem has been common practice in neural networks, solving planning problems such as the Tower-of-Hanoi (TOH) has been difficult in neural networks. The differences between the scheduling and planning problems are identified here from the neural network perspective, based on which an approach to solve planning problems with learning is proposed. In particular, the TOH is chosen as the target problem and represented as an array of neurons. A set of constraints derived from the TOH is formulated based on this representation. The system is designed to learn to generate legal moves. Learning legal moves is accomplished by generating illegal states and by measuring the legality of the states. Simulation results show that the system moves in a direction in which it learns legal moves for the TOH.

Original languageEnglish (US)
Title of host publicationProc 2 Int IEEE Conf Tools Artif Intell
PublisherPubl by IEEE
Pages366-371
Number of pages6
ISBN (Print)0818620846
StatePublished - Dec 1 1990
Externally publishedYes
EventProceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence - Herndon, VA, USA
Duration: Nov 6 1990Nov 9 1990

Publication series

NameProc 2 Int IEEE Conf Tools Artif Intell

Other

OtherProceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence
CityHerndon, VA, USA
Period11/6/9011/9/90

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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