Kevin LIU - Academia.edu (original) (raw)
Papers by Kevin LIU
International Journal on Artificial Intelligence Tools, 2000
The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and ... more The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to c...
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999
In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to b... more In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfill the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003
Approximate reasoning with words is one of the remarkable human capability that manipulates perce... more Approximate reasoning with words is one of the remarkable human capability that manipulates perceptions in a wide variety of physical and mental tasks whether in fuzzy or uncertain surroundings. To model this remarkable human capability, L.A. Zadeh (1999) proposed a new concept of "computing with words", which is a methodology in which the objects of computation are words and propositions drawn from a natural language. It provides a basis for a computational theory to imitate how humans make perception-based rational decisions in a fuzzy environment. Besides fuzziness, humans also perform perception-based reasoning well under uncertain circumstances. To deal with uncertain information in reasoning methods, several formalisms have been proposed, such as certainty factor (Shortliffe 1976), probabilistic logic (Nilsson 1986), Dempster-Shafer theory of evidence (Sha fer 1976), possibilistic logic (Dubois et al. 1994), and possibilistic reasoning (Lee et al. 2000), etc. An adequate management of uncertainty for reasoning methods has become a significant issue (Bonissone 1985).
International Journal on Artificial Intelligence Tools, 2000
The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and ... more The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to c...
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999
In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to b... more In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfill the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003
Approximate reasoning with words is one of the remarkable human capability that manipulates perce... more Approximate reasoning with words is one of the remarkable human capability that manipulates perceptions in a wide variety of physical and mental tasks whether in fuzzy or uncertain surroundings. To model this remarkable human capability, L.A. Zadeh (1999) proposed a new concept of "computing with words", which is a methodology in which the objects of computation are words and propositions drawn from a natural language. It provides a basis for a computational theory to imitate how humans make perception-based rational decisions in a fuzzy environment. Besides fuzziness, humans also perform perception-based reasoning well under uncertain circumstances. To deal with uncertain information in reasoning methods, several formalisms have been proposed, such as certainty factor (Shortliffe 1976), probabilistic logic (Nilsson 1986), Dempster-Shafer theory of evidence (Sha fer 1976), possibilistic logic (Dubois et al. 1994), and possibilistic reasoning (Lee et al. 2000), etc. An adequate management of uncertainty for reasoning methods has become a significant issue (Bonissone 1985).