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169
The Spatial Semantic Hierarchy
 Artificial Intelligence
, 2000
"... The Spatial Semantic Hierarchy is a model of knowledge of largescale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and ..."
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Cited by 248 (28 self)
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The Spatial Semantic Hierarchy is a model of knowledge of largescale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and mapbuilding. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problemsolving. The control level represents useful patterns of sensorimotor interaction with the world in the form of trajectoryfollowing and hillclimbing control laws leading to locally distinctive states. Local geometric maps in local frames of reference can be constructed at the control level to serve as observers for control laws in particular neighborhoods. The causal level abstracts continuous behavior among distinctive states into a discrete model ...
The Paradoxical Success of Fuzzy Logic
 IEEE Expert
, 1993
"... Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to twovalued logic. Moreover, there are few if any ..."
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Cited by 71 (1 self)
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Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to twovalued logic. Moreover, there are few if any published reports of expert systems in realworld use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledgebased systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledgebased systems. 1
Evolving fuzzy rule based controllers using genetic algorithms
 FUZZY SETS AND SYSTEMS
, 1996
"... The synthesis of geneticsbased machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multivariate nonlinear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presente ..."
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Cited by 46 (1 self)
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The synthesis of geneticsbased machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multivariate nonlinear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to geneticsbased machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (PFCS1) is proposed. PFCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rulesets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using PFCS1 are reported and compared with other published results. Application of PFCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.
A Hierarchy of Qualitative Representations for Space
 In Working papers of the Tenth International Workshop on Qualitative Reasoning about Physical Systems (QR96
, 1996
"... . Research in Qualitative Reasoning builds and uses discrete symbolic models of the continuous world. Inference methods such as qualitative simulation are grounded in the theory of ordinary differential equations. We argue here that cognitive mapping  building and using symbolic models of the ..."
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Cited by 36 (7 self)
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. Research in Qualitative Reasoning builds and uses discrete symbolic models of the continuous world. Inference methods such as qualitative simulation are grounded in the theory of ordinary differential equations. We argue here that cognitive mapping  building and using symbolic models of the largescale spatial environment  is a highly appropriate domain for qualitative reasoning research. We describe the Spatial Semantic Hierarchy (SSH), a set of distinct representations for space, each with its own ontology, each with its own mathematical foundation, and each abstracted from the levels below it. At the control level, the robot and its environment are modeled as a continuous dynamical system, whose stable equilibrium points are abstracted to a discrete set of "distinctive states." Trajectories linking these states can be abstracted to actions, giving a discrete causal graph level of representation for the state space. Depending on the properties of the actions, th...
What nonlinearity to choose? Mathematical foundations of fuzzy control
 Proceedings of the 1992 International Conference on Fuzzy Systems and Intelligent Control
, 1992
"... Abstract. Fuzzy control is a very successful way to transform the expert’s knowledge of the type “if the velocity is big and the distance from the object is small, hit the brakes and decelerate as fast as possible ” into an actual control. To apply this transformation one must: 1) choose fuzzy varia ..."
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Cited by 25 (18 self)
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Abstract. Fuzzy control is a very successful way to transform the expert’s knowledge of the type “if the velocity is big and the distance from the object is small, hit the brakes and decelerate as fast as possible ” into an actual control. To apply this transformation one must: 1) choose fuzzy variables corresponding to words like “small”, “big”; 2) choose operations corresponding to “and ” and “or”; 3) choose a method that transforms the resulting fuzzy variable for a into a single value ā. The wrong choice can drastically affect the quality of the resulting control, so the problem of choosing the right procedure is very important. From mathematical viewpoint these choice problems correspond to nonlinear optimization and are therefore extremely difficult. We develop a new mathematical formalism (based on group theory) that allows us to solve the problem of optimal choice and thus: 1) explain why the existing choices are really the best (in some situations); 2) explain a rather mysterious fact that the fuzzy control based on the experts’ knowledge is often better than the control by these same experts; 3) give choice recommendations for the cases when traditional choices do not work. Perspectives of space applications will be also discussed.
OPTIMAL STRATEGY OF SWITCHING REASONING METHODS IN FUZZY CONTROL
, 1994
"... Fuzzy control is a methodology that transforms control rules (described by an expert in words of a natural language) into a precise control strategy. There exist several versions of this transformation. The main difference between these versions is in how they interpret logical connectives “and” and ..."
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Cited by 21 (19 self)
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Fuzzy control is a methodology that transforms control rules (described by an expert in words of a natural language) into a precise control strategy. There exist several versions of this transformation. The main difference between these versions is in how they interpret logical connectives “and” and “or”, i.e., in other words, what reasoning method a version uses. Which of these versions should we choose? It turns out that on different stages of control, different reasoning methods lead to better control results. Therefore, a natural idea is not to fix a reasoning method once and forever, but to switch between different reasoning methods. In this chapter, we describe the optimal choice of the reasoning methods between which we switch.
The Composition and Validation of Heterogeneous Control Laws
, 1993
"... We present a method for creating and validating a nonlinear controller by the composition of heterogeneous local control laws appropriate to different operating regions. Like fuzzy logic control, these methods apply even in the presence of incomplete knowledge of the structure of the system, the bou ..."
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Cited by 20 (9 self)
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We present a method for creating and validating a nonlinear controller by the composition of heterogeneous local control laws appropriate to different operating regions. Like fuzzy logic control, these methods apply even in the presence of incomplete knowledge of the structure of the system, the boundaries of the operating regions, or even the control action to take. Unlike fuzzy logic control, these methods can be analyzed by a combination of classical and qualitative methods. Each operating region of the system has a classical control law, which provides highresolution control and can be analyzed by classical methods. Operating regions are defined by fuzzy set membership functions. The global control law is the weighted average of the local control laws, where the weights are provided by the operating region membership functions. A heterogeneous control law can be analyzed, even in the presence of incomplete knowledge, by representing it as a qualitative differential equation and usi...
A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection
 IEEE Transactions on Fuzzy Systems
, 2007
"... Abstract—Linguistic fuzzy modeling allows us to deal with the modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the acc ..."
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Cited by 20 (13 self)
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Abstract—Linguistic fuzzy modeling allows us to deal with the modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the accuracy of the linguistic model arises when complex systems are modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to linguistic fuzzy modeling without losing the interpretability to a high level. In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining linguistic models with higher levels of accuracy while maintaining good interpretability. To do so, we consider a new rule representation scheme base on the linguistic 2tuples representation model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in highdimensional problems and its ability to cooperate with methods to remove unnecessary rules. Index Terms—Fuzzy rulebased systems, genetic algorithms, interpretability, linguistic 2tuples representation, rule selection, tuning. I.
Adaptive fuzzy control for intervehicle gap keeping
 IEEE Transactions on Intelligent Transportation Systems
, 2003
"... Abstract—There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adapti ..."
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Cited by 18 (3 self)
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Abstract—There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adaptive cruise control (ACC). The main features of this kind of controller are the adaptation of the speed of the car to a predefined one and the keeping of a safe gap between the controlled car and the preceding vehicle on the road. We present an ACC controller based on fuzzy logic, which assists the speed and distance vehicle control, offering driving strategies and actuation over the throttle of a car. The driving information is supplied by the car tachometer and a RTK differential GPS, and the actuation over the car is made through an electronic interface that simulates the electrical signal of the accelerator pedal directly to the onboard computer. This control is embedded in an automatic driving system installed in two testbed massproduced cars instrumented for testing the work of these controllers in a real environment. The results obtained in these experiments show a very good performance of the gap controller, which is adaptable to all the speeds and safe gap selections. Index Terms—Autonomous vehicles, longitudinal control, intelligent vehicles, field experiments, fuzzy logic, adaptive cruise control (ACC), safe gap, Stop&Go, platoon driving, wireless communications, intelligent transportation systems (ITS). I.
Evolutionary Design of a Helicopter Autopilot
 in 3rd Online World Conf. on Soft Computing (WSC3
, 1998
"... This paper presents an evolutionary design method for fuzzy logic controllers, which is based on a selforganizing process that learns the appropriate relationship between control input and output. Our approach employs an evolution strategy that operates on vectors of real numbers which correspond t ..."
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Cited by 14 (2 self)
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This paper presents an evolutionary design method for fuzzy logic controllers, which is based on a selforganizing process that learns the appropriate relationship between control input and output. Our approach employs an evolution strategy that operates on vectors of real numbers which correspond to the gain factors in the conclusion part of fuzzy rules. An incremental learning scheme gradually expands the genome and thereby refines the fuzzy knowledge base that acquires additional fuzzy rules. The paper describes the hybrid architecture of a flight vehicle management system, which governs the operation of an autonomous model helicopter. The autopilot is composed of four modules that control the longitudinal and lateral motion, altitude and heading. The autopilot that constitutes the continuous regulation layer of the hybrid system is implemented by a set of fuzzy controllers. The evolutionary algorithm optimizes the fuzzy rule bases offline. We compare two design approaches, learnin...