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Fundamental Concepts of Qualitative Probabilistic Networks
 ARTIFICIAL INTELLIGENCE
, 1990
"... Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability dist ..."
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Cited by 119 (6 self)
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Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability distribution over the variables. Although these constraints are insufficient to determine probabilities uniquely, they are designed to justify the deduction of a class of relative likelihood conclusions that imply useful decisionmaking properties. Two types of qualitative relationship are defined, each a probabilistic form of monotonicity constraint over a group of variables. Qualitative influences describe the direction of the relationship between two variables. Qualitative synergies describe interactions among influences. The probabilistic definitions chosen justify sound and efficient inference procedures based on graphical manipulations of the network. These procedures answer queries about qualitative relationships among variables separated in the network and determine structural properties of optimal assignments to decision variables.
Statistical Foundations for Default Reasoning
, 1993
"... We describe a new approach to default reasoning, based on a principle of indifference among possible worlds. We interpret default rules as extreme statistical statements, thus obtaining a knowledge base KB comprised of statistical and firstorder statements. We then assign equal probability to all w ..."
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Cited by 45 (8 self)
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We describe a new approach to default reasoning, based on a principle of indifference among possible worlds. We interpret default rules as extreme statistical statements, thus obtaining a knowledge base KB comprised of statistical and firstorder statements. We then assign equal probability to all worlds consistent with KB in order to assign a degree of belief to a statement '. The degree of belief can be used to decide whether to defeasibly conclude '. Various natural patterns of reasoning, such as a preference for more specific defaults, indifference to irrelevant information, and the ability to combine independent pieces of evidence, turn out to follow naturally from this technique. Furthermore, our approach is not restricted to default reasoning; it supports a spectrum of reasoning, from quantitative to qualitative. It is also related to other systems for default reasoning. In particular, we show that the work of [ Goldszmidt et al., 1990 ] , which applies maximum entropy ideas t...
Adaptive Goal Recognition
 In IJCAI97  Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence
, 1997
"... Because observing the same actions can warrant different conclusions depending on who executed the actions, a goal recognizer that works well on one person might not work well on another. Two problems that arise in providing userspecific recognition are how to consider the vast number of possi ..."
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Cited by 33 (0 self)
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Because observing the same actions can warrant different conclusions depending on who executed the actions, a goal recognizer that works well on one person might not work well on another. Two problems that arise in providing userspecific recognition are how to consider the vast number of possible adaptations that might be made to the goal recognizer and how to evaluate a particular set of adaptations. For the first problem, we evaluate the use of hillclimbing to search the space of all combinations of an input set of adaptations. For the second problem, we present an algorithm that estimates the accuracy and coverage of a recognizer on a set of action sequences the individual has recently executed. We use these techniques to construct Adapt, a recognizerindependent unsupervisedlearning algorithm for adapting a recognizer to a person's idiosyncratic behaviors. Our experiments in two domains show that applying Adapt to the BOCE recognizer can improve its performance ...
Resolving plan ambiguity for cooperative response generation
 In Proc. 12th IJCAI
, 1991
"... Recognizing the plan underlying a query aids in the generation of an appropriate response. In this paper, we address the problem of how to generate cooperative responses when the user's plan is ambiguous. We show that it is not always necessary to resolve theambiguity, and provide a procedure that e ..."
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Cited by 27 (2 self)
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Recognizing the plan underlying a query aids in the generation of an appropriate response. In this paper, we address the problem of how to generate cooperative responses when the user's plan is ambiguous. We show that it is not always necessary to resolve theambiguity, and provide a procedure that estimates whether the ambiguity matters to the task of formulating a response. If the ambiguity does matter, we propose to resolve the ambiguity byentering into a clari cation dialogue with the user and provide a procedure that performs this task. Together, these procedures allow a questionanswering system to take advantage of the interactive and collaborative nature of dialogue in recognizing plans and resolving ambiguity. 1
Abductive Interpretation And Reinterpretation Of Natural Language Utterances
, 1993
"... To decide how to respond to an utterance, a speaker must interpret what others have said and why they have said it. Speakers rely on their expectations to decide whether they have understood each other. Misunderstandings occur when speakers differ in their beliefs about what has been said or why. If ..."
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Cited by 17 (6 self)
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To decide how to respond to an utterance, a speaker must interpret what others have said and why they have said it. Speakers rely on their expectations to decide whether they have understood each other. Misunderstandings occur when speakers differ in their beliefs about what has been said or why. If a listener hears something that seems inconsistent, he may reinterpret an earlier utterance and respond to it anew. Otherwise, he assumes that the conversation is proceeding smoothly. Recognizing an inconsistency as a misunderstanding and generating a new reply together accomplish what is known as a fourthposition repair. To model the repair of misunderstandings, this thesis combines both intentional and social accounts of discourse, unifying theories of speech act production, interpretation, and repair. In intentional accounts, speakers use their beliefs, goals, and expectations to decide what to say; when they interpret an utterance, speakers identify goals that might account for it. In...
A Modal Logic for Subjective Default Reasoning
, 1994
"... In this paper we introduce DML: Default Modal Logic. DML is a logic endowed with a twoplace modal connective that has the intended meaning of "If ff, then normally fi". On top of providing a welldefined tool for analyzing common default reasoning, DML allows nesting of the default operator. We ..."
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Cited by 16 (0 self)
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In this paper we introduce DML: Default Modal Logic. DML is a logic endowed with a twoplace modal connective that has the intended meaning of "If ff, then normally fi". On top of providing a welldefined tool for analyzing common default reasoning, DML allows nesting of the default operator. We present a semantic framework in which many of the known default proof systems can be naturally characterized, and prove soundness and completeness theorems for several such proof systems. Our semantics is a "neighbourhood modal semantics", and it allows for subjective defaults, that is, defaults may vary among different worlds within the same model. The semantics has an appealing intuitive interpretation and may be viewed as a settheoretic generalization of the probabilistic interpretations of default reasoning. We show that our semantics is most general in the sense that any modal semantics that is sound for some basic axioms for default reasoning is a special case of our semantics. Such a generality result may serve to provide a semantical analysis of the relative strength of different proof systems and to show the nonexistence of semantics with certain properties. 2 1
Learning to Reason: The NonMonotonic Case
, 1995
"... We suggest a new approach for the study of the nonmonotonicity of human commonsense reasoning. The two main premises that underlie this work are that commonsense reasoning is an inductive phenomenon, and that missing information in the interaction of the agent with the environment may be as informat ..."
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Cited by 15 (8 self)
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We suggest a new approach for the study of the nonmonotonicity of human commonsense reasoning. The two main premises that underlie this work are that commonsense reasoning is an inductive phenomenon, and that missing information in the interaction of the agent with the environment may be as informative for future interactions as observed information. This intuition is formalized and the problem of reasoning from incomplete information is presented as a problem of learning attribute functions over a generalized domain. We consider examples that illustrate various aspects of the nonmonotonic reasoning phenomena, which have been used over the years as "benchmarks" for various formalisms, and translate them into Learning to Reason problems. We demonstrate that these have concise representations over the generalized domain and prove that these representations can be learned efficiently. The framework developed suggests an "operational " approach to studying reasoning that is nevertheless ...
DecisionTheoretic Defaults
 In Proceedings of Canadian Society for Computational Studies of Intelligence Conference
, 1992
"... This paper considers defaults as summaries of decisiontheoretic deliberations. We investigate the idea that the default e ! a means that a is the optimal action based on all we know (contingently) being e. It is shown how this notion of a default is nonmonotonic and has a preference for more specif ..."
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Cited by 11 (0 self)
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This paper considers defaults as summaries of decisiontheoretic deliberations. We investigate the idea that the default e ! a means that a is the optimal action based on all we know (contingently) being e. It is shown how this notion of a default is nonmonotonic and has a preference for more specific defaults. It has the advantage of defaults can, in principle, be derived from lower level concepts. We thus have a rational basis for determining whether a default is correct or not. One special case considered is where the action is whether to accept some proposition as true, accept it as false or neither. This is needed to allow for conclusions to be used as premises in other defaults. It is shown that when the gain in utility of accepting a proposition depends only only on the truth of the proposition, then the acceptance of q based on evidence e depends only on whether P (qje) exceeds a threshold that is a function of the utilities for accepting q. We also give a bound on the loss (...
Some Varieties of Qualitative Probability
 Proceedings of the 5th International Conference on Information Processing and the Management of Uncertainty
, 1994
"... In this essay I present a general characterization of qualitative probability, including a partial taxonomy of possible approaches. I discuss some of these in further depth, identify central issues, and suggest some general comparisons. 1. Introduction In the standard theory of probability, degree ..."
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Cited by 9 (1 self)
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In this essay I present a general characterization of qualitative probability, including a partial taxonomy of possible approaches. I discuss some of these in further depth, identify central issues, and suggest some general comparisons. 1. Introduction In the standard theory of probability, degrees of belief for events or propositions take values in the real interval [0,1]. From degrees of belief on the primitive propositions, the theory dictates degrees of belief for various compound and conditional propositions, and vice versa. Computational schemes for probabilistic reasoning apply this theory to the automated derivation of degrees of belief for designated propositions of interest given prespecified degrees of belief over some other propositions and some particular conditioning propositions observed or hypothesized. This approach has, among other advantages, those accruing to a well understood and powerful underlying theory. Despite these virtues, many have objected to the straig...
Reference classes and multiple inheritances
 International Journal of Uncertainty, Fuzziness and and Knowledgebased Systems
, 1995
"... The reference class problem in probability theory and the multiple inheritances (extensions) problem in nonmonotonic logics can be referred to as special cases of con icting beliefs. The current solution accepted in the two domains is the speci city priority principle. By analyzing an example, seve ..."
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Cited by 7 (7 self)
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The reference class problem in probability theory and the multiple inheritances (extensions) problem in nonmonotonic logics can be referred to as special cases of con icting beliefs. The current solution accepted in the two domains is the speci city priority principle. By analyzing an example, several factors (ignored by the principle) are found to be relevant to the priority of a reference class. A new approach, NonAxiomatic Reasoning System (NARS), is discussed, where these factors are all taken into account. It is argued that the solution provided by NARS is better than the solutions provided by probability theory and nonmonotonic logics. 1