Neural network learning and expert systems
Neural network learning and expert systems
A fuzzy Petri net for knowledge representation and reasoning
Information Processing Letters
A high level net approach for discovering potential inconsistencies in fuzzy knowledge bases
Fuzzy Sets and Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Petri Net Theory and the Modeling of Systems
Petri Net Theory and the Modeling of Systems
Fuzzy Reasoning in Information, Decision, and Control Systems
Fuzzy Reasoning in Information, Decision, and Control Systems
Knowledge Representation Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
Uncertainty Management in Expert Systems Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
A High-Level Petri Net for Goal-Directed Semantics of Horn Clause Logic
IEEE Transactions on Knowledge and Data Engineering
Net-Based Computational Models of Knowledge-Processing Systems
IEEE Expert: Intelligent Systems and Their Applications
An Introduction to Learning Fuzzy Classifier Systems
Learning Classifier Systems, From Foundations to Applications
Multi-knowledge extraction and application
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A multilevel weighted fuzzy reasoning algorithm for expert systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A reasoning algorithm for high-level fuzzy Petri nets
IEEE Transactions on Fuzzy Systems
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In cooperative multiple robot systems (CMRS), dynamic knowledge representation and inference is the key in scheduling robots to fulfill the cooperation requirements.The first goal of this work is to use rough set based rules generation method to extract dynamic knowledge of our CMRS. Kang's rough set based rules generation method is used to get fuzzy dynamic knowledge from practical decision data. The second goal of this work is to use Fuzzy Neural Petri nets (FNPN) to represent and infer the dynamic knowledge on the base of dynamic knowledge extraction with self-learning ability. In particular, we investigate a new way to extract, represent and infer dynamic knowledge with self-learning ability in CMRS. Finally, the effectiveness of the dynamic knowledge extraction, representation and inference procedure are demonstrated.