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Reasoning about Beliefs and Actions under Computational Resource Constraints
 In Proceedings of the 1987 Workshop on Uncertainty in Artificial Intelligence
, 1987
"... ion Modulation In many cases, it may be more useful to do normative inference on a model that is deemed to be complete at a particular level of abstraction than it is to do an approximate or heuristic analysis of a model that is too large to be analyzed under specific resource constraints. It may pr ..."
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Cited by 179 (18 self)
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ion Modulation In many cases, it may be more useful to do normative inference on a model that is deemed to be complete at a particular level of abstraction than it is to do an approximate or heuristic analysis of a model that is too large to be analyzed under specific resource constraints. It may prove useful in many cases to store several beliefnetwork representations, each containing propositions at different levels of abstraction. In many domains, models at higher levels of abstraction are more tractable. As the time available for computation decreases, network modules of increasing abstraction can be employed. ffl Local Reformulation Local reformulation is the modification of specific troublesome topologies in a belief network. Approximation methods and heuristics designed to modify the microstructure of belief networks will undoubtedly be useful in the tractable solution of large uncertainreasoning problems. Such strategies might be best applied at knowledgeencoding time. An...
Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study
 In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence
, 1989
"... We define and exercise the expected value of computation as a fundamental component of reflection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reflection and action under scarce resources, and discuss how the techniques have been appli ..."
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Cited by 80 (8 self)
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We define and exercise the expected value of computation as a fundamental component of reflection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reflection and action under scarce resources, and discuss how the techniques have been applied in a highstakes medical domain. The work centers on endowing a computational agent with the ability to harness incomplete characterizations of problemsolving performance to control the amount of effort applied to a problem or subproblem, before taking action in the world or turning to another problem. We explore the use of the techniques in controlling decisiontheoretic inference itself, and pose the approach as a model of rationality under scarce resources. 1 Reflection and Flexibility Reflection about the course of problem solving and about the interleaving of problem solving and physical activity is a hallmark of intelligent behavior. Applying a portion of available reasoning resour...
Fuzzy sets and probability : Misunderstandings, bridges and gaps
 In Proceedings of the Second IEEE Conference on Fuzzy Systems
, 1993
"... This paper is meant to survey the literature pertaining to this debate, and to try to overcome misunderstandings and to supply access to many basic references that have addressed the "probability versus fuzzy set" challenge. This problem has not a single facet, as will be claimed here. Moreover it s ..."
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Cited by 39 (5 self)
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This paper is meant to survey the literature pertaining to this debate, and to try to overcome misunderstandings and to supply access to many basic references that have addressed the "probability versus fuzzy set" challenge. This problem has not a single facet, as will be claimed here. Moreover it seems that a lot of controversies might have been avoided if protagonists had been patient enough to build a common language and to share their scientific backgrounds. The main points made here are as follows. i) Fuzzy set theory is a consistent body of mathematical tools. ii) Although fuzzy sets and probability measures are distinct, several bridges relating them have been proposed that should reconcile opposite points of view ; especially possibility theory stands at the crossroads between fuzzy sets and probability theory. iii) Mathematical objects that behave like fuzzy sets exist in probability theory. It does not mean that fuzziness is reducible to randomness. Indeed iv) there are ways of approaching fuzzy sets and possibility theory that owe nothing to probability theory. Interpretations of probability theory are multiple especially frequentist versus subjectivist views (Fine [31]) ; several interpretations of fuzzy sets also exist. Some interpretations of fuzzy sets are in agreement with probability calculus and some are not. The paper is structured as follows : first we address some classical misunderstandings between fuzzy sets and probabilities. They must be solved before any discussion can take place. Then we consider probabilistic interpretations of membership functions, that may help in membership function assessment. We also point out nonprobabilistic interpretations of fuzzy sets. The next section examines the literature on possibilityprobability transformati...
Reasoning about the value of decisionmodel refinement: Methods and application
 In UAI '93
, 1993
"... We investigate the value of extending the completeness of a decision model along different dimensions of re nement. Speci cally, we analyze the expected value of quantitative, conceptual, and structural re nement of decision models. We illustrate the key dimensions of re nement with examples. The an ..."
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Cited by 12 (2 self)
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We investigate the value of extending the completeness of a decision model along different dimensions of re nement. Speci cally, we analyze the expected value of quantitative, conceptual, and structural re nement of decision models. We illustrate the key dimensions of re nement with examples. The analyses of value of model re nement can be used to focus the attention of an analyst or an automated reasoning system on extensions of a decision model associated with the greatest expected value. 1
A Bayesian perspective on confidence
 in Uncertainty in Artificial Intelligence
, 1989
"... We present a representation of partial confidence in belief and preference that is consistent with the tenets of decisiontheory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the a ..."
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Cited by 7 (0 self)
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We present a representation of partial confidence in belief and preference that is consistent with the tenets of decisiontheory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the assessment may improve decisions to which it is relevant. We show how a traditional decisionanalytic approach can be used to balance the benefits of additional modeling with associated costs. The approach can be used during knowledge acquisition to focus the attention of a knowledge engineer or expert on parts of a decision model that deserve additional refinement. 1
Precision–imprecision equivalence in a broad class of imprecise hierarchical uncertainty models
 Journal of Statistical Planning and Inference
, 2000
"... ABSTRACT. Hierarchical models are rather common in uncertainty theory. They arise when there is a ‘correct ’ or ‘ideal ’ (socalled firstorder) uncertainty model about a phenomenon of interest, but the modeler is uncertain about what it is. The modeler’s uncertainty is then called secondorder unce ..."
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Cited by 5 (1 self)
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ABSTRACT. Hierarchical models are rather common in uncertainty theory. They arise when there is a ‘correct ’ or ‘ideal ’ (socalled firstorder) uncertainty model about a phenomenon of interest, but the modeler is uncertain about what it is. The modeler’s uncertainty is then called secondorder uncertainty. For most of the hierarchical models in the literature, both the first and the secondorder models are precise, i.e., they are based on classical probabilities. In the present paper, I propose a specific hierarchical model that is imprecise at the second level, which means that at this level, lower probabilities are used. No restrictions are imposed on the underlying firstorder model: that is allowed to be either precise or imprecise. I argue that this type of hierarchical model generalizes and includes a number of existing uncertainty models, such as imprecise probabilities, Bayesian models, and fuzzy probabilities. The main result of the paper is what I call Precision–Imprecision Equivalence: the implications of the model for decision making and statistical reasoning are the same, whether the underlying firstorder model is assumed to be precise or imprecise. 1.
Review of uncertainty reasoning approaches as guidance for maritime and offshore safetybased assessment
 Journal of UK Safety and Reliability Society
, 2003
"... Many different formal techniques have been developed over the past two decades for dealing with uncertain information for decision making. In this paper we review some of the most important ones, i.e., Bayesian theory of probability, DempsterShafer theory of evidence, and fuzzy set theory, describe ..."
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Cited by 4 (4 self)
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Many different formal techniques have been developed over the past two decades for dealing with uncertain information for decision making. In this paper we review some of the most important ones, i.e., Bayesian theory of probability, DempsterShafer theory of evidence, and fuzzy set theory, describe how they work and in what ways they differ from one another, and show their strength and weakness respectively as well as their connection. We also consider hybrid approaches which combine two or more approximate reasoning techniques within a single reasoning framework. These have been proposed to address limitations in the use of individual techniques. The study is intended to provide guidance in the process of developing frameworks for safetybased decision analysis using different methods for reasoning under uncertainty. 1. Uncertainty in Decision Making In conventional information processing techniques it is often assumed that problems are well structured, complete information is always available and information processing procedures can be clearly defined. However, in many realworld decision making problems, this is not always the case and decisions making is often associated with uncertainty. “Uncertainty ” is a context dependent concept. There does not exist a comprehensive and unique definition of uncertainty. One definition of uncertainty is given as follows [Zimmermann 2000]: “Uncertainty is a situation in which a person does not have the quantitatively and qualitatively appropriate
UtilityBased Categorization
, 1993
"... The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utilitybased approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choi ..."
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Cited by 3 (1 self)
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The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utilitybased approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choice of appropriate actions. This is in contrast to classical and similaritybased approaches which seek logical completeness in concept description with respect to sensory data rather than actionoriented e#ectiveness. Utilitybased categorization is normative and not descriptive. It prescribes howanintelligent agent ought to conceptualize to act e#ectively. It provides ideals for categorization, speci#es criteria for the design of e#ective computational agents, and provides a model of ideal competence. A decisiontheoretic framework for utilitybased categorization whichinvolves reasoning about alternative categorization models of varying levels of abstraction is proposed. Categorization mode...
Indirect Adaptive Fuzzy Controllers
 INTERNATIONAL JOURNAL OF CONTROL
, 1992
"... Many classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings a ..."
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Cited by 3 (0 self)
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Many classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings and timevarying environmental conditions. The large number of possible future platforms leads to the desire to produce motion controllers which are generally applicable to a wide range of vehicles with little a priori knowledge of vehicle dynamics. Intelligent, selflearning, systems promise many of the desired features for such controllers. This thesis investigates the use of intelligent controllers for autonomous land vehicle motion control. A new class of fuzzy controller, the indirect adaptive fuzzy controller is proposed as...
Modeling Vague Beliefs Using FuzzyValued Belief Structures
, 1998
"... This paper presents a rational approach to the representation and manipulation of imprecise degrees of belief in the framework of evidence theory. We adopt as a starting point the non probabilistic interpretation of belief functions provided by Smets' Transferable Belief Model, as well as previous g ..."
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Cited by 3 (2 self)
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This paper presents a rational approach to the representation and manipulation of imprecise degrees of belief in the framework of evidence theory. We adopt as a starting point the non probabilistic interpretation of belief functions provided by Smets' Transferable Belief Model, as well as previous generalizations of evidence theory allowing to deal with fuzzy propositions. We then introduce the concepts of intervalvalued and fuzzyvalued belief structures, defined, respectively, as crisp and fuzzy sets of belief structures verifying hard or elastic constraints. We then proceed with a generalization of various concepts of DempsterShafer theory including those of belief and plausibility functions, combination rules and normalization procedures. Most calculations implied by the manipulation of these concepts are based on simple forms of linear programming problems for which analytical solutions exist, making the whole scheme computationally tractable. We discuss the application of this ...