Adaptive systems are systems whose characteristics evolve over time to improve their performance at a task. A fairly new area of study is that of adaptive rule-based systems. The system studied for this thesis uses meta-knowledge about rules, rulesets, rule performance, and system performance in order to improve its overall performance in a problem domain. An interesting and potentially important phenomenon which emerged is that the performance the system learns while solving a problem appears to be limited by an inherent break-even level of complexity. That is, the cost to the system of acquiring complexity does not exceed its benefit for that problem. If the problem is made more difficult, however, more complexity is required, the benefit of complexity becomes greater than its cost, and the system complexity begins increasing, ultimately to the new break-even point. There is no apparent ultimate limit to the complexity attainable.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/276534 |
| Date | January 1987 |
| Creators | Stackhouse, Christian Paul, 1960- |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Thesis-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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