Truth maintenance system
From Wikipedia, the free encyclopedia
 |
This article contains too much jargon and may need simplification or further explanation. Please discuss this issue on the talk page, and/or remove or explain jargon terms used in the article. Editing help is available. (September 2007) |
 |
This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations where appropriate. (September 2009) |
A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies. The name truth maintenance is due to the ability of these systems to restore consistency.
It is also termed as a belief revision system, a truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. If the current believed statements contradict the knowledge in KB, then the KB is updated with the new knowledge. It may happen that the same data will again come into existence, the previous knowledge will be required in KB. If the previous data is not present, it is required for new inference. But if the previous knowledge was with KB, then no retracing of the same knowledge was needed. Hence the use of TMS to avoid such retracing; it keeps track of the contradictory data with the help of a dependency record. This record reflects the retractions and additions which makes the inference engine (IE) aware of its current belief set.
Each statements having at least one valid justification is made a part of the current belief set. When a contradiction is found,the statement(s) responsible for the contradiction are identified and an appropriate is retraced. This results the addition of new statements to the KB. This process is called dependency-directed backtracking.
The TMS maintain the records in the form of a dependency network. The nodes in the network are one of the entries in the KB (may be a premise, antecedent, inference rule etc.) Each arc of the network represent the inference steps from which the node was derived.
Premise: A premise is a fundamental belief which is assumed to be always true. They do not need justifications. Considering premises are base from which justifications for all other nodes will be stated.
There are two types of justification for each node. They are:
- Support List [SL]
- Conceptual Dependencies(CP)
Many kinds of truth maintenance systems exist. Two major types are single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (database). Multi-context systems allow consistency to be relevant to a subset of facts in memory (a context) according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines. de Kleer's ATMS (1986) was utilized in systems based upon KEE on the Lisp Machine. The first multi-agent TMS was created by Mason and Johnson. It was a multi-context system. Bridgeland and Huhns created the first single-context multi-agent system.
[edit] See also
[edit] References
- Bridgeland, D. M. & Huhns, M. N., Distributed Truth Maintenance. Proceedings of. AAAI–90: Eighth National Conference on Artificial Intelligence, 1990.
- J. de Kleer (1986). An assumption-based TMS. Artificial Intelligence, 28:127–162.
- J. Doyle. A Truth Maintenance System. AI. Vol. 12. No 3, pp. 251–272. 1979.
- U. Junker and K. Konolige (1990). Computing the extensions of autoepistemic and default logics with a truth maintenance system. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI'90), pages 278–283. MIT Press.
- Mason, C. and Johnson, R. DATMS: A Framework for Assumption Based Reasoning, in Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Inc., 1989.
- D-A. McAllster. A three valued maintenance system. Massachusetts Institute of Technology, Artificial Intelligence Laboratory. AI Memo 473. 1978.
- G. M. Provan (1988). A complexity analysis of assumption-based truth maintenance systems. In B. Smith and G. Kelleher, editors, Reason Maintenance Systems and their Applications, pages 98–113. Ellis Horwood, New York.
- G. M. Provan (1990). The computational complexity of multiple-context truth maintenance systems. In Proceedings of the Ninth European Conference on Artificial Intelligence (ECAI'90), pages 522–527.
- R. Reiter and J. de Kleer (1987). Foundations of assumption-based truth maintenance systems: Preliminary report. In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI'87), pages 183–188. PDF
[edit] External links
 |
This artificial intelligence-related article is a stub. You can help Wikipedia by expanding it. |
