CONTENTS -- Foreword D. Ruan -- Invited Lectures -- Uncertainty Modelling in Real-Time: From Logistics to Financial Management H.-J. Zimmermann 1 -- Reasoning with Uncertain Intelligence Information R.R. Yager -- An Introduction to a Self Organizable & Regulating Engine P. P. Wang and J. Robinson -- Hand Gesture as a Means of Human-Friendly Interfacehteraction Z. Bien, J.-H. Do, J.-B. Kim, H. Jang, and D.-J. Kim -- 1. Basics on Hand Gesture -- 1.1. Definition of Gesture -- 1.2. Taxonomy of Hand Gesture -- 1.3. Hand Gesture Recognition -- 2. Application Systems -- 2.1. Soft Remocon System -- 2.2. Hand-signal Recognition System -- 2.3. Sign Language Recognition System -- 3. Concluding Remark -- Acknowledgments -- References -- Higher-Order Neural Units for Image Processing and Their Applications to Robot Routing Problems Z.-G. Hou, K.-I: Song, and M.M. Gupta -- 1. Introduction -- 2. Higher-order neural units -- 3. Edge detection using higher-order neural units and applications to mobile robot routing problems -- 4. Conclusions -- Acknowledgments -- References -- Classifiers and Decision Makers J. Montero -- 1. Introduction -- 2. Crisp consistency in decision making -- 3. Fuzzy consistency in decision making -- 4. Consistency in group decision making -- 5. Final comments -- Acknowledgments -- References -- Fuzzy Data Mining: Fuzziness/Uncertainty in Discovery of Associations G. Chen -- Uncertain Information Processing: From Theory to Implementations at Southwest Jiaotong University in China Y. Xu -- PART 1: FOUNDATIONS OF COMPUTATIONAL INTELLIGENCE -- Progress and Prospect in Lattice-Valued Logic Systems Based on Lattice Implication Algebras Y. Xu, D. Ruan, and J. Liu -- 1. Introduction -- 2. Research Progress in Lattice-Valued Logic -- 2.1 Progress in LIAs -- 2.1.1 The structures of LIAs -- 2.1.2 Filters of LIAs -- 2.1.3 LI-ideals of LIAs.

2.1.4 Topology of LIAs -- 2.2 Progress in Lattice-Valued Logic -- 2.2.1 Lattice-valued propositional logic LP(X) andfirst-order logic LF(X) -- 2.2.3 Lattice-valued propositional logic Lvpl and first-order logic Lvfl -- 2.3 Progress in Uncertainty Reasoning Based on Lattice-Valued Logic -- 2.4 Progress in Automatic Reasoning Based on Lattice-Valued Logic -- 3. Prospect on Lattice-Valued Logic Based on LIAs -- 3.1 Prospect on LIAs -- 3.2 Prospect on Lattice-Valued Logic Based on LIA -- 3.3 Prospect on Uncertainty Reasoning in Lattice-Valued Logic -- 3.4 Prospect on Automatic Reasoning in Lattice-Valued Logic -- Acknowledgement -- References -- Implication Algebra by Meta-Theory of the Uncertainty G. Resconi -- 1. Introduction -- 2. Lattice Implication Algebras Axioms -- 3. Meta Theory of Uncertainty and Implication Algebra -- 4. Local and Global Transformatin of Logic Value in the Set of Worlds -- Reference -- A Method of Constructing a Quasi-Lattice Implication Algebra C. Zeng -- 1 . The problem concerned -- 2. Algorithm -- 3. Example -- 4. Conclusion -- Acknowledgments -- References -- On the Construction of Finite Lattice Implication Algebras Y. Zhu -- 1. Introduction -- 2. Preliminaries -- 3. Results and Proofs -- Acknowledgments -- References -- The Relations between Lattice Implication Algebras and Brouwerian Lattice X . Pan, Y. Xu, and J. Liu -- 1. Introduction -- 2. Preliminaries -- 3. Relations between Brouwerian lattice and lattice implication algebras -- 4. Conclusion -- 5. Acknowledgements -- References -- Relating Intuitionistic Fuzzy Sets and Interval-Valued Fuzzy Sets Through Bilattices O. Arieli, C. Cornelis, G. Deschrijver, and E.E. Kerre -- 1. Motivation -- 2. Preliminaries: IFSs, IVFSs, and Bilattices -- 2.1. Intuitionistic fuzzy sets (IFSs) -- 2.2. Interval-valued fizzy sets (IVFSs) -- 3. Squares and Triangles -- 3.1. Squares.

3.2. Triangles -- 4. Concluding Remarks -- References -- Some Fuzzy LI-Ideals in Lattice Implication Algebras Y.L. Liu, S.Y. Liu, and Y. Xu -- 1. Introduction -- 2. Preliminaries -- 3. Fuzzy ILI-ideals of lattice implication algebras -- 4. Fuzzy prime LI-ideal of lattice implication algebras -- 5. Fuzzy LI-ideal characterizations of lattice implication algebras -- References -- LI-Filters in Lattice Implication Algebras Y.L. Liu, Y. Xu, and S.Y. Liu -- 1. Introduction -- 2. Preliminaries -- 3. F (L) as a Brouwerian Lattice -- 4. IF (L) as a Boolean algebra -- 5. Some algebraic properties of fuzzy LI-filters -- 6. Level LI-filter characterizations of fuzzy LI-filters -- References -- The Properties of Implication Operator in Lattice Implication Algebra L. Yi, Y. Xu, and Z. Pei -- 1. Introduction -- 2. The equivalent definition of lattice implication algebra -- 3. The operation of implication operator in lattice implication algebra -- References -- L-Valued Modal Fuzzy Reasoning Based on the Lv PL Z. Ping and Y. Xu -- 1. Introduction -- 2. L-valued modal fuzzy reasoning -- Acknowledgement -- References -- A Resolution Method on Lattice-Valued Tense Propositional Logic W Li and Y. Xu -- 1. Introduction -- 2. ( , t ) - Resolution Principle on LTP(x) -- 3. ( , t ) - Resolution Algorithm on LTP(x) -- 4. Conclusions -- Acknowledgments -- References -- Lattice Implication Algebra Model of Linguistic Variable Truth and Its Inference Z. Pei and Y. Xu -- 1. Introduction -- 2. Ordering structure of linguistic domain -- 3. Lattice implication algebra model of (T', c) -- 4. Reasoning by directly handling linguistic terms -- 5. Conclusion -- References -- The Logic Validity of Analogy Inference and Learning W. Pan and Y. Pan -- 1. Introduction -- 2. The Unified Representation of Inference Problem -- 3. Constraints on Logic Valid Analogy Inference in UOL.

4. The Logic Validity of Semantic Similarity -- 5. Conclusion -- References -- -Automated Reasoning Method Based on LP(X) W Wang, B. Jiang, and Y. Xu -- 1. Introduction -- 2. a-resolution field of 0-IESFs and 1-IESFs -- 3. -Automated Reasoning Method Based on LP(X) -- 4. Conclusion -- References -- Neural Networks = Fuzzy Rule Bases E. Kolman and M. Margaliot -- 1. Introduction -- 2. All-Permutations Fuzzy Rule-Base -- 3. Application: Rule Extraction -- 4. Conclusions -- References -- Analysis of Weight Characteristics in a Multilayer Perceptron W Kastnel -- T Forstel -- and R. Hampel -- 1. Introduction -- 2. Generation of the evaluation values for weight topology -- 2.1. DeJinition of weight factors -- 2.2. Definition of the correlation weight -- 2.3. Relation to statistical values -- 3. Demonstration of the methodology on an example -- 4. Conclusions -- References -- Immune Emergent Computation-Based Bio-Network Architecture and Its Simulation Platform X. Zhang, I: Ding, L. Ren, and L. Gao -- 1. Introduction -- 2. Immune Emergent Computation -- 3. The Design of Immune-Based Bio-network Architecture -- 3.1 Bio-entity and its Survivable Environment -- 3.2 Bio-network Core Services -- 3.3 Bio-network Low-Level Functional Modules -- 3.4 Bio-network Simulation Platform -- 4. Conclusions -- Acknowledgments -- Reference -- Fuzzy Analysis and Calculation Based on the Structured Element S. Guo -- 1. Preliminaries -- 2. Fuzzy structured element and fuzzy number -- 3. Analytic representations of Fuzzy-valued functions -- 4. Differential and integral of Fuzzy-valued function -- References -- Ranking Fuzzy Numbers with the General Characteristic Z. Wang and Q. Mou -- 1. Introduction -- 2. Corresponding notations and definitions -- 3. The Proposed Comparison Function -- 4. Numerical Examples -- 5. Conclusion -- References.

An Inclusion Measure for Rough Sets Z. Deng and Y. Xu -- 1. Introduction -- 2. A New Inclusion Measure -- 3. Relations to Other Concepts -- References -- A Semantic Model of Linguistic Truth Variable Z. Wu and Y. Xu -- 1. Introduction -- 2. A Semantic Model of Linguistic Truth Variable -- 2.1 A Linguistic Truth Variable Team -- 2.2 Construct A Complete Linguistic Truth Variable Team -- 2.3 A Semantic Model of Linguistic Truth Variable -- 3. Example -- 4. Conclusion -- References -- A Multi-Attribute Evaluating Approach based on Analysis and Learning of Attribute Coordinate J. Feng and N. Lu -- 1. Introduction -- 2. Local most satisfactory solution -- 3. Local most satisfactory solutions set (or mind standard level) -- 4. Local and Global satisfactory degree problem -- 5. Concluding remarks -- Reference -- PART 2: KNOWLEDGE DISCOVERY AND DECISION MAKING -- Mining Causality from Imperfect Data L. J. Mazlack -- 1. Introduction -- 1.1. Data Mining, Introduction -- 1.2. Naive Association Rules Can Lead To Bad Decisions -- 2. Causality -- 3. Recognizing Causality Basics -- 4. Problems With Using Probability -- 5. Epilogue -- Acknowledgment -- References -- Discovering Frequent Patterns in Sequence Groups J. W. Guan, D.A. Bell, and D. Y. Liu -- 1 Introduction -- 2 Sequences and Containing Relations -- 3 Frequent Patterns Contained in a Sequence Group -- 4 Theorems for Finding Patterns -- 5 Summary and Future Work -- References -- Empirically Validating an Adapted Classification Based on Associations Algorithm on UCI Data D. Janssens, Y. Lun, G. Wets, G. Chen, and T: Brijs -- 1. Introduction -- 2. Classification based on associations -- 3. Identifying weaknesses and proposing adaptations to CBA -- 3.1. Limits of conditional probability (confidence) -- 3.2. Intensity of implication -- 4. Empirical section -- 5. Conclusion -- Acknowledgement -- References.

A Method for Association Rules Mining J. Ma, G. Chen, E. E. Kerre, and D. Ruan.