4 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Combining Relevant Expert Knowledge Using Mixtures of Feedback Fuzzy Cognitive Maps
Olaoluwa Adigun, Akash Kumar Panda, Bart Kosko
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 im…
Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators
Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents. The approach uses feedback fuzzy cognitive maps (FCM…
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mi…
The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs
Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
We design a large-language-model (LLM) agent system that extracts causal feedback fuzzy cognitive maps (FCMs) from raw text. The causal learning or extraction process is agentic bo…
Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent
Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
A large language model (LLM) can map a feedback causal fuzzy cognitive map (FCM) into text and then reconstruct the FCM from the text. This explainable AI system approximates an id…