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The Process Theoretical Approach to Qualitative DEVS
 Proc. 1996 Conference on AI, Simulation, and Planning in High Autonomy Systems
, 1996
"... Qualitative extensions to the Discrete EVent Systems (devs) formalism are presented based on some new ideas relating general process and automata theory and General Information Theory (git). Specifically, we will consider the applicability of possibilistic and fuzzy processes and automata to the de ..."
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Cited by 6 (2 self)
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Qualitative extensions to the Discrete EVent Systems (devs) formalism are presented based on some new ideas relating general process and automata theory and General Information Theory (git). Specifically, we will consider the applicability of possibilistic and fuzzy processes and automata to the devs methodology. Much of the devs formalism can be cast into classical finite automata theoretical terms. Classical automata generalize to qualitative temporal processes with input and output where states are valued on a lattice and the state transition function is implemented by algebraic semiring operators. Special cases include classical deterministic, nondeterministic, and stochastic (Markov) processes, and the newer fuzzy and possibilistic processes. In this way devs can be extended qualitatively in terms of general Moore automata. 1 Introduction The Discrete EVent Systems (devs) methodology [30] has emerged in recent years as an important modeling and simulation formalism. devs is an a...
An ObjectOriented Architecture for Possibilistic Models
 in: Proc. 1994 Conf. ComputerAided Systems Technology
, 1994
"... . An architecture for the implementation of possibilistic models in an objectoriented programming environment (C++ in particular) is described. Fundamental classes for special and general random sets, their associated fuzzy measures, special and general distributions and fuzzy sets, and possibilist ..."
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Cited by 4 (4 self)
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. An architecture for the implementation of possibilistic models in an objectoriented programming environment (C++ in particular) is described. Fundamental classes for special and general random sets, their associated fuzzy measures, special and general distributions and fuzzy sets, and possibilistic processes are specified. Supplementary methodsincluding the fast Mobius transform, the maximum entropy and Bayesian approximations of random sets, distribution operators, compatibility measures, consonant approximations, frequency conversions, and possibilistic normalization and measurement methodsare also introduced. Empirical results to be investigated are also described. 1 Introduction Possibility theory [4] is an alternative information theory to that based on probability. Although possibility theory is logically independent of probability theory, they are related: both arise in DempsterShafer evidence theory as fuzzy measures defined on random sets; and their distributions a...
Aggregation and Completion in Probability and Possibility Theory
, 1994
"... this paper some new ideas about the mathematical relations between probability and possibility are presented in the context of random set theory. Although some of these results are already known, I believe that the concepts of complete random sets and distributions, and numerical and structural aggr ..."
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this paper some new ideas about the mathematical relations between probability and possibility are presented in the context of random set theory. Although some of these results are already known, I believe that the concepts of complete random sets and distributions, and numerical and structural aggregation functions on them, provide a broad, consistent context not only in which to place probability and possibility, but also to consider new forms of measures and distributions which have yet to be studied. Below is a synoptic summary of the mathematical ideas to be presented in the full paper. 2 General Random Sets and Distributions
Possibilistic Systems Within a General Information Theory
, 1999
"... We survey possibilistic systems theory and place it in the context of Imprecise Probabilities and General Information Theory (git). In particular, we argue that possibilistic systems hold a distinct position within a broadly conceived, synthetic git. Our focus is on systems and applications which ar ..."
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We survey possibilistic systems theory and place it in the context of Imprecise Probabilities and General Information Theory (git). In particular, we argue that possibilistic systems hold a distinct position within a broadly conceived, synthetic git. Our focus is on systems and applications which are semantically grounded by empirical measurement methods (statistical counting), rather than epistemic or subjective knowledge elicitation or assessment methods. Regarding fuzzy measures as special previsions, and evidence measures (belief and plausibility measures) as special fuzzy measures, thereby we can measure imprecise probabilities directly and empirically from setvalued frequencies (random set measurement). More specifically, measurements of random intervals yield empirical fuzzy intervals. In the random set (DempsterShafer) context, probability and possibility measures stand as special plausibility measures in that their "distributionality" (decomposability) maps directly to an "a...