Fuzzy Cognitive Maps

Also known as MAP

Latest papers

9 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Combining Relevant Expert Knowledge Using Mixtures of Feedback Fuzzy Cognitive Maps
Nov 3, 2026Olaoluwa Adigun, Akash Kumar Panda, Bart KoskoFuzzy Cognitive Maps Mixture-of-Experts Models
School of Computing & Information Sciences, Florida International University, Miami, USA · Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA
Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps
Sep 30, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data Generation Synthetic Tabular Data Generation
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
Sep 28, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data Generation Synthetic Tabular Data Generation
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators
Sep 14, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoCausal World Models Fuzzy Cognitive Maps
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA · School of Computing and Information Sciences, Florida International University Miami, FL 33199, USA
LLM for the development of FCM
Jul 6, 2026Alexis KafantarisFuzzy Cognitive Maps
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
May 18, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoComputational Cognitive Modeling Bayesian Inference
University of Southern California, Los Angeles, CA 90007, USA · Florida International University, Miami, FL 33199, USA
Attractor FCM
Apr 30, 2026Alexis KafantarisRecurrent Neural Networks Physics-Informed ML
Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
Apr 6, 2026Jose L. SalmeronFuzzy Cognitive Maps Fuzzy Logic
School of Engineering, CUNEF Universidad, Madrid, Spain
Looking Through Glass Box
Mar 6, 2026Alexis KafantarisFuzzy Cognitive Maps Fuzzy Logic
Athens University of Economics and Business

Nov 3, 2026J/K move · Enter open · S save

Combining Relevant Expert Knowledge Using Mixtures of Feedback Fuzzy Cognitive Maps

Olaoluwa Adigun, Akash Kumar Panda, Bart Kosko

School of Computing & Information Sciences, Florida International University, Miami, USA · Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA

Fuzzy Cognitive Maps Mixture-of-Experts Models Neuro-Symbolic Reasoning

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A neuro-symbolic system should combine only relevant answers from expert agents or other knowledge sources. Using only relevant experts for a given input query becomes ever more important for accuracy and scalability as the knowledge network grows and connects ever more knowledge sources. We extend the standard definition of relevance from the legal theory of evidence: An answer is relevant if and only if it is both probative and material for the matter at hand given the knowledge available. We apply this relevancy scheme to mixing the feedback fuzzy cognitive maps (FCMs) of real or artificial experts. The expert FCM directed causal graphs mix or combine into a common knowledge network. We compare the equilibrium limit-cycle predictions or answers of the experts to the answers of the mixture FCM and then estimate the probative status of answers by their difference or variation. A new Bayes-like theorem de-mixes the knowledge mixture into corresponding posterior-like FCMs that also allow relevancy-based comparisons. The result is a final set of relevancy mixing weights that produces a relevancy-mixed FCM knowledge network for the given query. So different queries produce different relevancy-mixed FCMs. We demonstrate this relevancy approach using generated synthetic FCMs from an actual 13-node expert FCM that models the causes and feedbacks of ischemic-stroke risk.