Constraints on tree structure in concept formation. (English) Zbl 0751.68056
Artificial intelligence, IJCAI-91, Proc. 12th Int. Conf., Sydney/Australia 1991, 810-816 (1991).
[For the entire collection see Zbl 0741.68016.]
The authors describe ARACHNE, a concept formation system that uses explicit constraints on tree structures and local restructuring operators to produce well-formed probabilistic concept trees, while maintaining high predictive accuracy. The paper also presents COBWEB [D. H. Fisher: Knowledge acquisition via incremental conceptual clustering, Machine Learning, No. 2, 139-172 (1987)], which employs different criteria for tree formation and uses alternative restructuring operators, and compares it to the authors’ ARACHNE in four experiments, the latter providing promising qualities.
The authors describe ARACHNE, a concept formation system that uses explicit constraints on tree structures and local restructuring operators to produce well-formed probabilistic concept trees, while maintaining high predictive accuracy. The paper also presents COBWEB [D. H. Fisher: Knowledge acquisition via incremental conceptual clustering, Machine Learning, No. 2, 139-172 (1987)], which employs different criteria for tree formation and uses alternative restructuring operators, and compares it to the authors’ ARACHNE in four experiments, the latter providing promising qualities.
Reviewer: N.Curteanu (Iaşi)
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |
| 68T35 | Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence |
