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A Multivalued Logic Approach to Integrating Planning and Control
 Artificial Intelligence
, 1995
"... Intelligent agents embedded in a dynamic, uncertain environment should incorporate capabilities for both planned and reactive behavior. Many current solutions to this dual need focus on one aspect, and treat the other one as secondary. We propose an approach for integrating planning and control base ..."
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Cited by 111 (9 self)
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Intelligent agents embedded in a dynamic, uncertain environment should incorporate capabilities for both planned and reactive behavior. Many current solutions to this dual need focus on one aspect, and treat the other one as secondary. We propose an approach for integrating planning and control based on behavior schemas, which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as predicates on these trajectories. Goals and behaviors can be combined to produce conjoint goals and complex controls. The ability of multivalued logics to represent graded preferences allows us to formulate tradeoffs in the combination. Two composition theorems relate complex controls to complex goals, and provide the key to using standard knowledgebased deliberation techniques to generate complex controllers. We report experiments in planning and execution on a mobi...
Integrating reactivity and goaldirectedness in a fuzzy controller
 Procs. of the 2nd FuzzyIEEE Conference
, 1993
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Soft Computing: the Convergence of Emerging Reasoning Technologies
 Soft Computing
, 1997
"... The term Soft Computing (SC) represents the combination of emerging problemsolving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to so ..."
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Cited by 58 (8 self)
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The term Soft Computing (SC) represents the combination of emerging problemsolving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, realworld problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagationtype algorithms.
Towards General Measures of Comparison of Objects
"... We propose a classification of measures enabling to compare fuzzy characterizations of objects, according to their properties and the purpose of their utilization. We establish ..."
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Cited by 55 (17 self)
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We propose a classification of measures enabling to compare fuzzy characterizations of objects, according to their properties and the purpose of their utilization. We establish
What Are Fuzzy Rules and How to Use Them
 Fuzzy Sets and Systems
, 1996
"... Fuzzy rules have been advocated as a key tool for expressing pieces of knowledge in "fuzzy logic". However, there does not exist a unique kind of fuzzy rules, nor is there only one type of "fuzzy logic". This diversity has caused many a misunderstanding in the literature of fuzzy ..."
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Cited by 38 (12 self)
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Fuzzy rules have been advocated as a key tool for expressing pieces of knowledge in "fuzzy logic". However, there does not exist a unique kind of fuzzy rules, nor is there only one type of "fuzzy logic". This diversity has caused many a misunderstanding in the literature of fuzzy control. The paper is a survey of different possible semantics for a fuzzy rule and shows how they can be captured in the framework of fuzzy set and possibility theory. It is pointed out that the interpretation of fuzzy rules dictates the way the fuzzy rules should be combined. The various kinds of fuzzy rules considered in the paper (gradual rules, certainty rules, possibility rules, and others) have different inference behaviors and correspond to various intended uses and applications. The representation of fuzzy unlessrules is briefly investigated on the basis of their intended meaning. The problem of defining and checking the coherence of a block of parallel fuzzy rules is also briefly addressed. This iss...
Rulebase structure identification in an adaptivenetworkbased fuzzy inference system
 IEEE Trans. Fuzzy Syst
, 1994
"... AbstructFuzzy rulebase modeling is the task of identifying the structure and the parameters of a fuzzy IFTHEN rule base so that a desired input/output mapping is achieved. Recently, using adaptive networks to finetune membership functions in a fuzzy rule base has received more and more attention ..."
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Cited by 26 (0 self)
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AbstructFuzzy rulebase modeling is the task of identifying the structure and the parameters of a fuzzy IFTHEN rule base so that a desired input/output mapping is achieved. Recently, using adaptive networks to finetune membership functions in a fuzzy rule base has received more and more attention. In this paper we summarize Jang’s architecture of employing an adaptive network and the Kalman filtering algorithm to identify the system parameters. Given a surface structure, the adaptively adjusted inference system performs well on a number of interpolation problems. We generalize Jang’s basic model so that it can be used to solve classification problems by employing parameterized tnorms. We also enhance the model to include weights of importance so that feature selection becomes a component of the modeling scheme. Next, we discuss two ways of identifying system structures based on Jang’s architecture. For the topdown approach, we summarize several ways of partitioning the feature space and propose a method of using clustering objective functions to evaluate possible partitions. We analyze the overall learning and operation complexity. In particular, we pinpoint the dilemma between two desired properties: modeling accuracy and pattern matching efficiency. Based on the analysis, we suggest a bottomup approach of using rule organization to meet the conflicting requirements. We introduce a data structure, called a fuzzy binary boxtree, to organize rules so that the rule base can be matched against input signals with logarithmic efficiency. To preserve the advantage of parallel processing assumed in fuzzy rulebased inference systems, we give a parallel algorithm for pattern matching with a linear speedup. Moreover, as we consider the communication and storage cost of an interpolation model, it is important to extract the essential components of the modeled system and use the rest as a backup. We propose a rule combination mechanism to build a simplified version of the original rule base according to a given focus set. This scheme can be used in various situations of pattern representation or data compression, such as in image coding or in hierarchical pattern recognition.
Measurement Of Membership Functions: Theoretical And Empirical Work
, 1995
"... This chapter presents a review of various interpretations of the fuzzy membership function together with ways of obtaining a membership function. We emphasize that different interpretations of the membership function call for different elicitation methods. We try to make this distinction clear u ..."
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Cited by 22 (1 self)
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This chapter presents a review of various interpretations of the fuzzy membership function together with ways of obtaining a membership function. We emphasize that different interpretations of the membership function call for different elicitation methods. We try to make this distinction clear using techniques from measurement theory.
The Relation between Inference and Interpolation in the Framework of Fuzzy Systems
, 1996
"... This papers aims at clarifying the meaning of different interpretations of the MaxMin or, more generally, the Maxtnorm rule in fuzzy systems. It turns out that basically two distinct approaches play an important role in fuzzy logic and its applications: fuzzy interpolation on the basis of an impr ..."
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Cited by 18 (1 self)
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This papers aims at clarifying the meaning of different interpretations of the MaxMin or, more generally, the Maxtnorm rule in fuzzy systems. It turns out that basically two distinct approaches play an important role in fuzzy logic and its applications: fuzzy interpolation on the basis of an imprecisely known function and logical inference in the presence of fuzzy information. Keywords: Fuzzy logic; fuzzy control; MaxMin rule, fuzzy interpolation. 1 Introduction This is a synthesizing paper which returns to the question, what is the role of the MaxMin (Maxtnorm) rule in fuzzy logic from the viewpoint of logical inference. We aim at demonstrating that two basic, more or less complementary approaches in fuzzy logic and its applications can be distinguished, namely: fuzzy interpolation of a fuzzily specified precise function and logical inference in the presence of fuzzy information. The first task is solved using the Maxtnorm rule which essentially leads to search of a fuzzy...
Using Fuzzy Logic For Mobile Robot Control
, 1997
"... : The development of techniques for autonomous operation in realworld, unstructured environments constitutes one of the major trends in the current research on mobile robotics. In spite of recent advances, a number of fundamental difficulties remain. In this chapter, we discuss how fuzzy logic techn ..."
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Cited by 18 (1 self)
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: The development of techniques for autonomous operation in realworld, unstructured environments constitutes one of the major trends in the current research on mobile robotics. In spite of recent advances, a number of fundamental difficulties remain. In this chapter, we discuss how fuzzy logic techniques can be used to address some of these difficulties. To illustrate the discussion, we describe the fuzzylogic solutions developed on Flakey, the mobile robot of SRI International. 5.1 INTRODUCTION Chapter 5 of the International Handbook of Fuzzy Sets D. Dubois, H. Prade and H. Zimmermann, editors Kluwer Academic Publisher, forthcoming in 1999 Contact: http://iridia.ulb.ac.be/saffiotti/ The operation of an autonomous mobile robot in a realworld unstructured environment requires consideration of multiple issues. First, the controller must be able to operate under conditions of imprecision and uncertainty. For example, prior knowledge about the environment is, in general, incomplete, unc...