Rule Based Fuzzy Cognitive Maps Research Articles

Evaluation of Information Security Risks with Use of Rule-Based Fuzzy Cognitive Maps

Evaluation of Information Security Risks with Use of Rule-Based Fuzzy Cognitive Maps

New Mechanisms for Reasoning and Impacts Accumulation for Rule-Based Fuzzy Cognitive Maps

Rule-based fuzzy cognitive maps (RBFCMs) have been developed for modeling nonmonotonic, uncertain, cause-effect systems. However, the standard reasoning and impact accumulation mechanisms developed for RBFCMs assume that the level of variation that a fuzzy set represents is directly linked with the shape of the fuzzy set. It poses a big restriction on how the corresponding fuzzy sets have to be constructed. In this paper, we propose a new reasoning and impact accumulation mechanisms which take into consideration standard semantics of fuzzy sets, where their uncertainty is measured by fuzziness. New type of complex fuzzy relationships and reasoning on them is introduced to model a joint impact of several causal nodes on one effect node. With these new mechanisms, RBFCMs become much more flexible, provide more means to capture complexity of real-world systems, and are less computational demanding than standard mechanisms. The advantages of the new RBFCMs are demonstrated using different examples and compared with standard mechanisms.

Rule based fuzzy cognitive maps and natural language processing in machine ethics

Purpose The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA). Design/methodology/approach The decision results derived by the AMA are acquired via fuzzy logic interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM). Findings Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge. Research limitations/implications This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles. Practical implications To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties. Originality/value This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics.

Simulating and Forecasting Qualitative Marcoeconomic Models Using Rule-Based Fuzzy Cognitive Maps

Simulating and Forecasting Qualitative Marcoeconomic Models Using Rule-Based Fuzzy Cognitive Maps

Qualitative modelling of fishermen's behaviour in a pelagic fishery

As managers and fishery scientists become increasingly aware of the need to understand fishermens’ behaviour when faced with new management strategies, there is a growing need to develop models that describe their decision-making processes and tools that are able to simulate the possible outcomes of alternative management scenarios. This paper shows how data collected on board fishing vessels, a source of data currently available for many fleets and fisheries around the world, can be combined with Rule Based Fuzzy Cognitive Maps, a modelling technique that is able to deal with both quantitative and qualitative data. Results show that the presented model is able to replicate with a reasonably precision the behaviour of a pelagic fishery. Results and the potential of Rule-Based Fuzzy Cognitive Maps to predict fisher's behaviour are discussed.

Qualitative optimization of Fuzzy Causal Rule Bases using Fuzzy Boolean Nets

Fuzzy Causal Rule Bases (FCRb) are widely used and are the most important rule bases in Rule Based Fuzzy Cognitive Maps (RB-FCM). However, FCRb are subject to several restrictions that create difficulties in their creation and completion. This paper proposes a method to optimally complete FCRb using Fuzzy Boolean Net properties as qualitative universal approximators. Although the proposed approach focuses on FCRb, it can be generalized to any fuzzy rule base.

A hybrid fuzzy cognitive model based on weighted OWA operators and single-antecedent rules

Conventional fuzzy cognitive maps (FCMs) can only represent monotonic or symmetric causal relationships and cannot simulate the AND/OR combinations of the antecedent nodes. The rule-based fuzzy cognitive maps (RBFCMs) usually suffer from the well-known combinatorial rule explosion problem. A hybrid fuzzy cognitive model based on weighted OWA operators and single-antecedent rules is proposed to eliminate the drawbacks of the existing FCM models. Hybrid fuzzy cognitive maps (HFCMs) represent the causal relationships with single-antecedent fuzzy rules and handle the various AND/OR relationships among the antecedent nodes with weighted OWA aggregation operators. Compared with conventional FCMs, HFCMs have more powerful cognitive capability. Compared with RBFCMs, HFCMs reduce the scale and complexity of the rule bases significantly and have better representation and inference performance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1189–1196, 2007.