Above are papers related to the following paper: [Paper_ID] Paper #1116044. 	[Title] A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL [Abstract] fl This paper will appear in Discourse, Interaction, and Communication, X. Arrazola, K. Korta, and F. J. Pelletier, eds., Kluwer, 1997. Much of this work was performed while the author was at IBM Almaden Research Center. IBM's support is gratefully acknowledged. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134307. 	[Title] Generalized Update: Belief Change in Dynamic Settings [Abstract] Belief revision and belief update have been proposed as two types of belief change serving different purposes. Belief revision is intended to capture changes of an agent's belief state reflecting new information about a static world. Belief update is intended to capture changes of belief in response to a changing world. We argue that both belief revision and belief update are too restrictive; routine belief change involves elements of both. We present a model for generalized update that allows updates in response to external changes to inform the agent about its prior beliefs. This model of update combines aspects of revision and update, providing a more realistic characterization of belief change. We show that, under certain assumptions, the original update postulates are satisfied. We also demonstrate that plain revision and plain update are special cases of our model, in a way that formally verifies the intuition that revision is suitable for static belief change. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #74698. 	[Title] Abduction to Plausible Causes: An Event-based Model of Belief Update [Abstract] The Katsuno and Mendelzon (KM) theory of belief update has been proposed as a reasonable model for revising beliefs about a changing world. However, the semantics of update relies on information which is not readily available. We describe an alternative semantical view of update in which observations are incorporated into a belief set by: a) explaining the observation in terms of a set of plausible events that might have caused that observation; and b) predicting further consequences of those explanations. We also allow the possibility of conditional explanations. We show that this picture naturally induces an update operator conforming to the KM postulates under certain assumptions. However, we argue that these assumptions are not always reasonable, and they restrict our ability to integrate update with other forms of revision when reasoning about action. fl Some parts of this report appeared in preliminary form as An Event-Based Abductive Model of Update, Proc. of Tenth Canadian Conf. on in AI, Banff, Alta., (1994). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134314. 	[Title] Modeling Belief in Dynamic Systems. Part I: Foundations [Abstract] Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations of belief change. We claim that a better understanding of belief change can be gained from examining appropriate semantic models. In this paper we propose a general framework in which to model belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes if he knows that is more plausible than :. We then consider some properties defining the interaction between knowledge and plausibility, and show how these properties affect the properties of belief. In particular, we show that by assuming two of the most natural properties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledge, plausibility (and hence belief), and time, which extends the framework of Halpern and Fagin for modeling knowledge in multi-agent systems. We then examine the problem of "minimal change". This notion can be captured by using prior plausibilities, an analogue to prior probabilities, which can be updated by "conditioning". We show by example that conditioning on a plausibility measure can capture many scenarios of interest. In a companion paper, we show how the two best-studied scenarios of belief change, belief revision and belief update, fit into our framework. ? Some of this work was done while both authors were at the IBM Almaden Research Center. The first author was also at Stanford while much of the work was done. IBM and Stanford's support are gratefully acknowledged. The work was also supported in part by the Air Force Office of Scientific Research (AFSC), under Contract F49620-91-C-0080 and grant F94620-96-1-0323 and by NSF under grants IRI-95-03109 and IRI-96-25901. A preliminary version of this paper appears in Proceedings of the 5th Conference on Theoretical Aspects of Reasoning About Knowledge, 1994, pp. 44-64, under the title "A knowledge-based framework for belief change, Part I: Foundations". Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #87915. 	[Title] On the Complexity of Conditional Logics [Abstract] Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants discussed in the literature. We show that, in general, deciding satisfiability is PSPACE-complete for formulas with arbitrary conditional nesting and NP-complete for formulas with bounded nesting of conditionals. However, we provide several exceptions to this rule. Of particular note are results showing that (a) when assuming uniformity (i.e., that all worlds agree on what worlds are possible), the decision problem becomes EXPTIME-complete even for formulas with bounded nesting, and (b) when assuming absoluteness (i.e., that all worlds agree on all conditional statements), the decision problem is NP-complete for for mulas with arbitrary nesting. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137130. 	[Title] On the Semantics of Belief Revision Systems [Abstract] We consider belief revision operators that satisfy the Alchourron-Gardenfors-Makinson postulates, and present an epistemic logic in which, for any such revision operator, the result of a revision can be described by a sentence in the logic. In our logic, the fact that the agent's set of beliefs is is represented by the sentence O, where O is Levesque's `only know' operator. Intuitively, O is read as ` is all that is believed.' The fact that the agent believes is represented by the sentence B , read in the usual way as ` is believed'. The connective represents update as defined by Katsuno and Mendelzon. The revised beliefs are represented by the sentence O B . We show that for every revision operator that satisfies the AGM postulates, there is a model for our epistemic logic such that the beliefs implied by the sentence O B in this model correspond exactly to the sentences implied by the theory that results from revising by . This means that reasoning about changes in the agent's beliefs reduces to model checking of certain epistemic sentences. The negative result in the paper is that this type of formal account of revision cannot be extended to the situation where the agent is able to reason about its beliefs. A fully introspective agent cannot use our construction to reason about the results of its own revisions, on pain of triviality. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134316. 	[Title] A Qualitative Markov Assumption and Its Implications for Belief Change [Abstract] The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surprising observation as a sign that previous beliefs were wrong, while update treats a surprising observation as an indication that the world has changed. In general, we would expect that an agent making an observation may both want to revise some earlier beliefs and assume that some change has occurred in the world. We define a novel approach to belief change that allows us to do this, by applying ideas from probability theory in a qualitative settings. The key idea is to use a qualitative Markov assumption, which says that state transitions are independent. We show that a recent approach to modeling qualitative uncertainty using plausibility measures allows us to make such a qualitative Markov assumption in a relatively straightforward way, and show how the Markov assumption can be used to provide an attractive belief-change model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #65074. 	[Title] Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. [Abstract] We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23069. 	[Title] On the Logic of Iterated Belief Revision [Abstract] We show in this paper that the AGM postulates are too week to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by proposing four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Contrary to the AGM framework, the proposed postulates characterize belief revision as a process which may depend on elements of an epistemic state that are not necessarily captured by a belief set. We also show that a simple modification to the AGM framework can allow belief revision to be a function of epistemic states. We establish a model-based representation theorem which characterizes the proposed postulates and constrains, in turn, the way in which entrenchment orderings may be transformed under iterated belief revision. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23070. 	[Title] A Knowledge-Based Framework for Belief Change Part I: Foundations [Abstract] We propose a general framework in which to study belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes ' if he knows that ' is true in all the worlds he considers most plausible. We then consider some properties defining the interaction between knowledge and plausibility, and show how these properties affect the properties of belief. In particular, we show that by assuming two of the most natural properties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledge, plausibility (and hence belief), and time, which extends the framework of Halpern and Fagin [HF89] for modeling knowledge in multi-agent systems. We show that our framework is quite expressive and lets us model in a natural way a number of different scenarios for belief change. For example, we show how we can capture an analogue to prior probabilities, which can be updated by "conditioning". In a related paper, we show how the two best studied scenarios, belief revision and belief update, fit into the framework. Please summarise all the given information.

Paper #1116044 has the title `A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL  
` and the abstract `fl This paper will appear in Discourse, Interaction, and Communication, X. Arrazola, K. Korta, and F. J. Pelletier, eds., Kluwer, 1997. Much of this work was performed while the author was at IBM Almaden Research Center. IBM's support is gratefully acknowledged. 
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Question: Which category should Paper #1116044 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1116044 has the title `A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL  
` and the abstract `fl This paper will appear in Discourse, Interaction, and Communication, X. Arrazola, K. Korta, and F. J. Pelletier, eds., Kluwer, 1997. Much of this work was performed while the author was at IBM Almaden Research Center. IBM's support is gratefully acknowledged. 
`.
Question: Which category should Paper #1116044 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods