Fuzzy qualitative reasoning (FQR) is a form of approximate reasoning that can be defined loosely as the fusion of Fuzzy Reasoning (FR) with Qualitative Reasoning (QR). Both these research areas have as one of their goals the construction of computational reasoning tools that can predict and explain the behaviour of, often dynamic, systems whose analytic relations are incompletely specified. Whe...

Due to the enormous success of fuzzy control, there has been a great interest in fuzzy reasoning, i.e. the formalization and application of human rule based knowledge. But when we take a closer look at the practical success of fuzzy reasoning, we nd that there are only simple rule bases involved. So far, we are not able to cope with complex fuzzy rule bases. Despite lots of theoretical investig...

Fuzzy systems address the imprecision of the input and output variables, which formally describe notions like “rather warm” or “pretty cold”, while provide a behaviour that depends on fuzzy data. This class of systems are classically represented by means of Fuzzy Inference Systems (FIS), a computing framework based on the concepts of fuzzy if-then rules and fuzzy reasoning. Even if FIS are larg...

In case of fuzzy reasoning in sparse fuzzy rule bases, the question of selecting the suitable fuzzy similarity measure is essential. The rule antecedents of the sparse fuzzy rule bases are not fully covering the input universe therefore fuzzy reasoning methods applied for sparse fuzzy rule bases requires similarity measures able to distinguish the similarity of non-overlapping fuzzy sets, too. ...

Fuzzy Description Logics (f-DLs) have been proposed as extensions of classical Description Logics able to handle imprecise and vague knowledge. Although several extensions to expressive DLs have been proposed today and many reasoning algorithms exist there is still no scalable and efficient reasoning system reported, mainly due to inherited computational complexity from crisp DLs and the lack f...

Collaborative agents are able to find global solutions to complex systems using local knowledge rather than a complete global knowledge set. The complexity, on the other hand, brings uncertainty. Global optimisation in a uncertain environment can be undertaken using fuzzy collaborative agents. Negotiation, involving generating, evaluating and modifying action plans, under fuzzy circumstance is ...

Some difficulties emerging during the construction of fuzzy behaviour-based control structures are inherited from the type of the applied fuzzy reasoning. The fuzzy rule base requested for many classical reasoning methods needed to be complete. In case of fetching fuzzy rules directly from expert knowledge e.g. for the behaviour coordination module, the way of building a complete rule base is n...

Due to the advantages of Fuzzy reasoning Petri-nets(FPN)on uncertain and incomplete information processing. It is a promising technique to solve the complex power system fault-section estimation problem. Therefore, we propose a novel estimation method based on Adaptive Fuzzy Petri Nets (AFPN), in this algorithm, the AFPN is used to build a dynamic fault diagnosis fuzzy reasoning model, where th...