LEHMANN D., "Belief Revision, Revised", Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995, p. 1534--1541.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... . The latter is clearly stronger. C3) says that a piece of evidence should be retained after accommodating a more recent evidence that entails given the current belief state. C4) simply says that no epistemic input can act as its own defeater. Arlo Costa and Parikh [2] and Lehmann [11] have shown that (C2) is problematic in allowing only the trivial update (this is done by considering the case when the formulas in question are not simple propositional atoms) Freund and Lehmann [6] have shown that it is inconsistent with the original AGM axioms for belief sets (as is the ....

Daniel Lehmann, `Belief revision, revised', in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1534--1540, (1995).

....BC Gammastructure, and how each BC Gammastructure defines a belief revision system. The aim of this paper is to explore if a similar correspondence with conditional logics can be obtained for iterated belief revision systems. Iterated belief revision has been widely investigated in recent years [2, 3, 11, 14]. In particular, it has been shown that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during the revision process. For this reason new postulates have been proposed which characterize belief revision as a process which may depend on elements of an ....

....by Darwiche and Pearl only requires that the syntactical form of the revision formula is irrelevant in determining the resulting belief set. We can prove the following: Lemma 2 (A1) A2) and (A3) can be derived from (C1) C2) C3) and (C4) together with (R 1) Gamma (R 6) Lehmann in [11] has proposed a set of rationality postulates for iterated revision. In his framework he represents the sequence of revisions applied to the initial belief set by a sequence of formulas oe and denotes by [oe] the resulting belief set. The belief set [oe : A] represents the result of revising the ....

D. Lehmann, Belief revision revised, in Proc. 14th International Joint Conference on Artificial Intelligence (IJCAI'95), pp. 1534--1540.

....is based on Fuhrmann and Hansson [9] Further changes to some of the parameters in the basic AGM framework include: 1. Relaxing the requirement that revisions be functional [24] 2. Change recording theory change, as opposed to knowledge adding theory change [19] 3. Iterated theory change [22, 20]. 4. Multi agent theory change [15] II Alternative Approaches In the second half of these notes we discuss alternative approaches to dealing with changing information; we start with non monotonic logic and verisimilitude, and then turn to descriptive approaches based on modal and dynamic logic. ....

D. Lehmann. Belief Revision Revised. In Proceedings IJCAI 95, 1995.

....does not depend on the syntax of the sentences. K 7 and K 8 regard revision after composite information; they are not much intuitive and can be replaced by the following [12] C1. K p) pErrore. Il segnalibro non definito.q = K (pErrore. Il segnalibro non definito.q) As Lehmann [13] showed, C1 is an elegant, slightly more powerful, version of the postulates K 7 and K 8 ; the idea is that if one learns, first, some partial information (p) and then the full information (pErrore. Il segnalibro non definito.q) the partial information should not make a difference. ....

Lehmann, `Belief Revision revised', Proc. 14th Inter. Joint Conf. on Artificial Intelligence, (1995) 1534-1540.

....as a constraint on iterated belief revision functions i.e. on belief states rather than on the revision of belief states. Before moving on, we should pause a little and compare the result of our search for the right framework with what appears to be the most similar approach in the literature. Lehmann (1995) introduces a revised framework for belief revision that is similarly based on unary revision functions that take sequences of sentences as input. However, this remarkable coincidence should not conceal the fact that the intentions of Lehmann s paper and the present one are entirely di#erent. ....

....take sequences of sentences as input. However, this remarkable coincidence should not conceal the fact that the intentions of Lehmann s paper and the present one are entirely di#erent. First, Lehmann speaks of the central concept of a belief state resulting from a finite sequence of revisions (Lehmann 1995, p. 1535, emphasis added) whereas we identify belief states with unary, iterated revision functions. Lehmann explicitly renounces any ambition to contribute to the epistemology of science or the ontology of belief revision (p. 1538) His paper is complementary to the present one in that he ....

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Lehmann, Daniel: 1995, `Belief Revision, Revised', in IJCAI'95 -- Proceedings of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, pp. 1534--1540.

....and K 0 = Cn(fxg) and let x and y be atomic sentences. It follows from 1 6 that K 0 :y = K x:y . Hence (K 0 :y ) y = K x:y ) y . Applying DP2 0 to this equality, we get K 0 y = K y , i.e. Cn(fx; yg) Cn(fyg) Contradiction Such considerations have led Lehmann [8] to further weaken DP2. In fact, it is possible to show that Lehmann s proposal is a special case of William s [13] adjustment operation. 2 Our 1 We are thankful to Daniel Lehmann for pointing this out in personal communication. 2 Lehmann s [8] Widening Ranked Model can be captured via ....

....Such considerations have led Lehmann [8] to further weaken DP2. In fact, it is possible to show that Lehmann s proposal is a special case of William s [13] adjustment operation. 2 Our 1 We are thankful to Daniel Lehmann for pointing this out in personal communication. 2 Lehmann s [8] Widening Ranked Model can be captured via William s [13] hx; ffi adjustment. Readers acquainted with the semantics of adjustment (see the principle OCF Adjs in [9] p. 364) will easily verify that hx; ffi adjustment, where ff is the next non empty rank greater than rank(x) corresponds to ....

Lehmann, D. "Belief Revision, Revised", in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, pp. 1534 -- 1540, August 1995.

....and defended by the authors, but, recently, some doubts have been expressed as to their desirability, at least for modeling updates, see [KM92] and, more importantly for us, it is not clear that the AGM postulates are all what one would like. A number of authors, in particular [FL94] DP94] [Leh95], have in fact argued that one would expect some additional postulates to hold. But the consideration of additional postulates has proved slippery and dangerous: the postulates proposed in [DP94] have been shown inconsistent in [Leh95] and have been modified in [DP97] But this modification forces ....

....would like. A number of authors, in particular [FL94] DP94] Leh95] have in fact argued that one would expect some additional postulates to hold. But the consideration of additional postulates has proved slippery and dangerous: the postulates proposed in [DP94] have been shown inconsistent in [Leh95] and have been modified in [DP97] But this modification forces on us the rejection of one of the basic ontological commitments of the AGM framework, which brings us to a second remark. Secondly, one of the basic ontological commitments of the AGM approach is that what the agent is revising is a ....

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D. Lehmann, "Belief Revision, revised", in "Proceedings of 14th IJCAI", pp. 1534--1541, Morgan Kaufmann, 1995.

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LEHMANN D., "Belief Revision, Revised", Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995, p. 1534--1541.

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Lehmann, `Belief Revision revised', Proc. 14th Inter. Joint Conf. on Artificial Intelligence, (1995) 1534-1540.

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