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46
Reputation and Social Network Analysis In MultiAgent Systems
, 2002
"... The use of previous direct interactions is probably the best way to calculate a reputation but, unfortunately this information is not always available. This is especially true in large multiagent systems where interaction is scarce. In this paper we present a reputation system that takes advantage, ..."
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Cited by 130 (8 self)
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The use of previous direct interactions is probably the best way to calculate a reputation but, unfortunately this information is not always available. This is especially true in large multiagent systems where interaction is scarce. In this paper we present a reputation system that takes advantage, among other things, of social relations between agents to overcome this problem.
Multivalued Logics: A Uniform Approach to Inference in Artificial Intelligence
 Computational Intelligence
, 1988
"... This paper describes a uniform formalization of much of the current work in AI on inference systems. We show that many of these systems, including firstorder theorem provers, assumptionbased truth maintenance systems (atms's) and unimplemented formal systems such as default logic or circumscriptio ..."
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Cited by 59 (0 self)
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This paper describes a uniform formalization of much of the current work in AI on inference systems. We show that many of these systems, including firstorder theorem provers, assumptionbased truth maintenance systems (atms's) and unimplemented formal systems such as default logic or circumscription can be subsumed under a single general framework. We begin by defining this framework, which is based on a mathematical structure known as a bilattice. We present a formal definition of inference using this structure, and show that this definition generalizes work involving atms's and some simple nonmonotonic logics. Following the theoretical description, we describe a constructive approach to inference in this setting; the resulting generalization of both conventional inference and atms's is achieved without incurring any substantial computational overhead. We show that our approach can also be used to implement a default reasoner, and discuss a combination of default and atms methods th...
Uncertainty, Belief, and Probability
 Computational Intelligence
, 1989
"... : We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and ..."
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Cited by 46 (2 self)
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: We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. In addition to removing the requirement that every event be assigned a probability, our approach circumvents other criticisms of probabilitybased approaches to uncertainty. For example, the measure of belief in an event turns out to be represented by an interval (defined by the inner and outer measure), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to an event E without committing to a belief about its negation :E (since the inner measure of an event plus the inner measure of its negation is not necessarily one). Interestingly enough, inner measures induced by probability measures turn out to correspond in a ...
Are artificial neural networks black boxes
 IEEE Trans. Neural Networks
, 1997
"... Abstract — Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisf ..."
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Cited by 38 (3 self)
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Abstract — Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rulebased systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of fduality. In addition, this interpretation offers an automated knowledge acquisition procedure. Index Terms — Equality between neural nets and fuzzy rulebased systems, fduality, fuzzy additive systems, interpretation of neural nets, ior operator. I.
FORMALISMS FOR NEGOTIATION IN ENGINEERING DESIGN
, 1996
"... Engineering projects often undergo several design iterations before being completed. Information received from other groups working on a project (analysis, manufacturing, marketing, sales) will often necessitate changes in a design. The interaction between different groups associated with a design p ..."
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Cited by 20 (5 self)
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Engineering projects often undergo several design iterations before being completed. Information received from other groups working on a project (analysis, manufacturing, marketing, sales) will often necessitate changes in a design. The interaction between different groups associated with a design project often takes the form of informal “negotiation. ” This form of interaction commonly arises when engineering information is imprecise. The Method of Imprecision (MoI) is a formal method for the representation and manipulation of preliminary and imprecise design information. It provides a mechanism for the formalization of these informal negotiations. The nature and scope of informal negotiation in engineering is explored and discussed, and application of the MoI is illustrated with an example.
A Behavioural Model For Linguistic Uncertainty
 INFORMATION SCIENCES
, 1998
"... The paper discusses the problem of modelling linguistic uncertainty, which is the uncertainty produced by statements in natural language. For example, the vague statement `Mary is young' produces uncertainty about Mary's age. We concentrate on simple affirmative statements of the type `subject is pr ..."
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Cited by 16 (3 self)
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The paper discusses the problem of modelling linguistic uncertainty, which is the uncertainty produced by statements in natural language. For example, the vague statement `Mary is young' produces uncertainty about Mary's age. We concentrate on simple affirmative statements of the type `subject is predicate', where the predicate satisfies a special condition called monotonicity. For this case, we model linguistic uncertainty in terms of upper probabilities, which are given a behavioural interpretation as betting rates. Possibility measures and probability measures are special types of upper probability measure. We evaluate Zadeh's suggestion that possibility measures should be used to model linguistic uncertainty and the Bayesian claim that probability measures should be used. Our main conclusion is that, when the predicate is monotonic, possibility measures are appropriate models for linguistic uncertainty. We also discuss several assessment strategies for constructing a numerical model.
Bayesian Network Modelling through Qualitative Patterns
 ARTIFICIAL INTELLIGENCE
, 2003
"... In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions o#ered by the Bayesiannetwork formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative an ..."
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Cited by 12 (5 self)
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In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions o#ered by the Bayesiannetwork formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They thus have the advantage that developers can abstract from the numerical detail, and therefore the gap may not be as wide as for their quantitative counterparts. A notion that has been suggested in the literature to facilitate Bayesiannetwork development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to o#er developers a highlevel starting point when developing Bayesian networks.
Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks
 IN S. GOONATILAKE AND S.KHEBBAL, EDITORS INTELLIGENT HYBRID SYSTEMS
, 1990
"... Many authors have suggested that SP (symbol processing) and CN (connectionist network) models offer radically, or even fundamentally, different paradigms for modeling intelligent behavior (see Schneider, 1987) and the design of intelligent systems. Others have argued that CN models have little to co ..."
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Cited by 9 (6 self)
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Many authors have suggested that SP (symbol processing) and CN (connectionist network) models offer radically, or even fundamentally, different paradigms for modeling intelligent behavior (see Schneider, 1987) and the design of intelligent systems. Others have argued that CN models have little to contribute to our efforts to understand intelligence (Fodor & Pylyshyn, 1988). A critical examination of the popular characterizations of SP and CN models suggests that neither of these extreme positions is justified. There are many advantages to be gained by a synthesis of the best of both SP and CN approaches in the design of intelligent systems. The Generalized connectionist networks (GCN) (alternately called generalized neuromorphic systems (GNS)) introduced in this paper provide a framework for such a synthesis.
Preliminary Vehicle Structure Design: AN INDUSTRIAL APPLICATION OF. . .
 DETC98/DTM5646, Proceedings of DETC'98, 1998 ASME Design Engineering Technical Conferences
, 1998
"... The Method of Imprecision, or M o I, is a formal method for incorporating imprecise information into a design process. This methodology has been exercised on a problem in preliminary vehicle structure design in collaboration with VW Wolfsburg. Results show that the method is useful in trading off mu ..."
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Cited by 8 (0 self)
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The Method of Imprecision, or M o I, is a formal method for incorporating imprecise information into a design process. This methodology has been exercised on a problem in preliminary vehicle structure design in collaboration with VW Wolfsburg. Results show that the method is useful in trading off multiple conflicting attributes, including styling preferences and engineering requirements. Keywords: Industrial Applications of DTM; Vehicle Structure Design; Design Methods and Models; Design Representations; Computational Methods of Design; Fuzzy Sets INTRODUCTION Preliminary design is inherently imprecise (Becker, 1973; Blockley, 1980; Gavin, 1994; Yao and Furuta, 1986), and many preliminary design decisions are made informally. Preliminary design has enormous economic importance, as much of the cost of a design is determined by these (often informal) preliminary decisions (Whitney, 1988). A further complication is the difficulty of communicating imprecise information between different...