Reason maintenance

Reason maintenance [1] [2] is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason for maintenance of the data base, which can be defeated , and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. [2] It encompasses a variety of technical That share a common architecture: [3] two components-a reasoner and a reason maintaining system-communicate with Each Other via an interface. The reasons for the inferences and justifications of the “reasons” for the inferences. The reason for this is also the fact that it is the basis of the assumptions. The reason for this is that the inconsistency is derived.

truth maintenance system , or TMS , is a knowledge representation method for both their beliefs and their dependencies and an algorithm called the “maintenance maintenance algorithm” that manipulates and maintains the dependencies. The term ” maintenance” refers to the ability to restore consistency.

A maintenance maintenance system (KB) through revision. If the current believed statements contradict the knowledge in the KB, then the KB is updated with the new knowledge. It may happen that the previous knowledge will be required in the KB. If the previous data is not present, it may be required for new inference. But if the previous knowledge was in the KB, then no retracing of the same knowledge is needed. The use of TMS avoids such retracing; It keeps track of the contradictory data with the help of a dependency record. This record reflects the reactions and additions that makes the inference engine (IE) aware of its current belief set.

Each statement has at least one valid justification. When a contradiction is found, the statement (s) responsible for the contradiction are identified and the records are appropriately updated. This process is called dependency-directed backtracking.

The TMS algorithm maintains the records in the form of a dependency network. Each node in the network is an entry in the KB (a premise, antecedent, or inference rule etc.).

A premise is a fundamental belief which is assumed to be true. They do not need justifications. The set of premises are the basis of which justifications for all other nodes will be derived.

There are two types of justification for a node. They are:

  1. Support List [SL]
  2. Conditional Proof (CP)

Many kinds of truth maintenance systems exist. Two major types are single-context and multi-contextual maintenance. In this paper, we present the results of the study of the consistency of the theory of consistency in classical logic . Multi-context systems support paraconsistency by allowing consistency to a subset of facts in memory, a context, according to the history of logical inference. This is achieved by tagging each deduction with its logical history. Multi-agent, multi-agent. Of Kleer’s assumption-based truth maintenance system (ATMS, 1986) was utilized in systems based on 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.

See also

  • Knowledge representation
  • Artificial Intelligence
  • Belief revision
  • Knowledge acquisition

References

  1. Jump up^ Doyle, J., 1983. The ins and outs of reason maintenance, in: Proceedings of the Eighth International Joint Conference on Artificial Intelligence – Volume 1, IJCAI’83. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 349-351.
  2. ^ Jump up to:b Doyle, J .: Truth maintenance systems for problem solving. Tech. Rep. AI-TR-419, Dep. Of Electrical Engineering and Computer Science of MIT (1978)
  3. Jump up^ McAllester, DA: Truth maintenance. AAAI90 (1990)

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