Chopra, S., Parikh, R., An Inconsistency Tolerant Model for Belief Representation and Belief Revision, Proc. IJCAI-99, Morgan Kaufmann, 1999  Home/Search   Document Not in Database   Summary   Related Articles   Check

....closed theories. Other approaches like those discussed in [11] consider finite sets of formulas, sometimes called belief bases, as epistemic states. A smaller fraction of work in belief revision has studied an obvious alternative: the revision of epistemic states expressed as nonmonotonic theories [4, 9, 16, 2, 6]. This is somewhat surprising since close relationships between properties of nonmonotonic inference relations and postulates for belief revision have been established [10] Indeed, one of the reasons why nonmonotonic logics were invented is their ability to handle conflicts and inconsistencies, ....

Chopra, S., Parikh, R., An Inconsistency Tolerant Model for Belief Representation and Belief Revision, Proc. IJCAI-99, Morgan Kaufmann, 1999

....to thank Samuel Buss, Melvin Fitting, Konstantinos Georgatos, Bud Mishra, Graham Priest and Renata Wassermann for helpful comments. Part of this research was carried out while both authors were visiting New York University and we thank NYU for its hospitality. Portions of this study appeared in [6]. 1 1 Introduction When we consider the belief structures of ordinary people as contrasted with formal logical theories, some features of the former immediately stand out. One feature is that people tend to divide their beliefs more or less roughly into subject areas and reason in actual ....

Samir Chopra and Rohit Parikh. An inconsistency tolerant model for belief representation and belief revision. In Proceedings of the 16th International Joint Conference on Articial Intelligence, pages 192-197. Morgan-Kaufmann, 1999.

.... with relevance may be found in [1] A permissive model of belief revision that retains weakenings of formulas is presented in [4] while an implementation of a AGM style revision system using first order theorem provers may be found in [6] Our model incorporates the insights offered in [11] and [3]. These models offered a syntactic definition of relevance based on sharing of non logical symbols between formulas and used it to guide the revision process. An extension to a first order setting was left as an open problem. We have presented a general model for first order belief revision ....

Samir Chopra and Rohit Parikh. An inconsistency tolerant model for belief representation and belief revision. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pages 192--197. Morgan-Kaufmann, 1999.

....heavy use of relevance relations amongst formulas in a belief base. These relevance relations serve as a particular instance of a more general model of context sensitive inference. The particular method presented here incorporates the insights in earlier proposals made by [Geo96] Par96] and [CP99]. Geo96] uses the linear order of a belief sequence as a prioritization to generate a variety of inference relations, shows that these schemes are non monotonic and, therefore, induce a method for belief revision. Par96] shows that if we have a theory referring to two or more disjoint subjects, ....

....is suggested that new information about one of them should not a ect any other. This ensures a relevance or context sensitive, localized notion of belief revision and serves as one way of capturing a more general notion of relatedness amongst propositions in a belief base (as studied by [Was99] [CP99] consider sets of theories called B structures, which are individually consistent, but can be jointly inconsistent, to capture the intuition that real agents often reason with an inconsistent, yet usable, set of beliefs which is divided into individually consistent compartments. Our (current) ....

[Article contains additional citation context not shown here]

Samir Chopra and Rohit Parikh. An inconsistency tolerant model for belief representation and belief revision. In Proceedings of the 16th International Joint Conference on Articial Intelligence, pages 192-197, MorganKaufmann, 1999.

....is a belief base has been noted by [Han92] and [Neb92] Similarly, the notion that a sequence of formulae captures the importance of temporal ordering is noted by [Leh95] We propose a method for inference from sequenced belief bases. This method augments recent proposals, Geo96] PAR96] and [CP99]. In [Geo96] it is shown that taking the linear order of a belief sequence as a prioritization generates a variety of inference relations. It is shown that all such schemes are non monotonic (rational inference) and, therefore, induce a method for belief revision. In [PAR96] it is shown that if ....

....if we have a theory referring to two disjoint subjects, then our language can be partitioned into corresponding sub languages, and it is suggested that new information about one of them should not affect the other. This ensures a relevance or context sensitive, localized notion of belief revision. [CP99] explicate the distinction between implicit and explicit beliefs by considering sets of theories called B structures, which are individually consistent, but can be jointly inconsistent. This captures the intuition that real agents often reason with an inconsistent, yet usable, set of beliefs. In ....

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Samir Chopra and Rohit Parikh. An inconsistency tolerant model for belief representation and belief revision. in IJCAI 99, to appear.

....J. of the IGPL, Vol. 0 No. 0, pp. 1 12 0000 c Oxford University Press 2 Approximate Belief Revision new information. There should be a way of isolating the subset of a belief base that contains the relevant beliefs for a query or an operation of belief change. Recent models such as [13] 9] [5], 6] 17] attempt to tackle the problem of plausible belief revision by using nonstandard inference operations such as local change operators and o ering structuring relations on belief bases via relevance sensitivity. In these frameworks for local reasoning and belief change, the key idea is ....

....S does not mention their atoms. This means that these irrelevant formulas will always generate inconsistent kernels which are singletons and therefore will be needlessly deleted by a contraction operation. We can choose to pre process the belief base eliminating irrelevant formulas as suggested in [5] and [17] we only revise the relevant component and keep the rest of the belief base unchanged. We now present a slightly di erent strategy which uses j= 1 S in a more sophisticated way. Recall from De nition 4.1 that R(p; q; B) if and only if there is a clause in B which has literals based on ....

Samir Chopra and Rohit Parikh. An inconsistency tolerant model for belief representation and belief revision. In Proceedings of IJCAI 99, Morgan Kaufmann. Full version to appear in AMAI.

....our beliefs is a belief base has been noted by [Han92] and [Neb92] the notion that a sequence of formulae captures the importance of temporal ordering is noted by [Leh95] We propose a method for inference from belief sequences. This method augments recent proposals, Geo96] PAR96] and [CP99]. In [Geo96] it is shown that taking the linear order of a belief sequence as a prioritization generates a variety of inference relations. It is shown that all such schemes are non monotonic (rational inference) and, therefore, induce a method for belief revision. In [PAR96] it is shown that if ....

....if we have a theory referring to two disjoint subjects, then our language can be partitioned into corresponding sub languages, and it is suggested that new information about one of them should not a ect the other. This ensures a relevance or context sensitive, localized notion of belief revision. [CP99] explicate the distinction between implicit and explicit beliefs by considering sets of theories called B structures, which are individually consistent, but can be jointly inconsistent. This captures the intuition that real agents often reason with an inconsistent, yet usable, set of beliefs. In ....

[Article contains additional citation context not shown here]

Samir Chopra and Rohit Parikh. An inconsistency tolerant model for belief representation and belief revision. in IJCAI 99, to appear.

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