TL;DR
This work presents a methodology for learning structures of successive events that occur in multi domain agent organisations and explains how MDL is used and how DFA models learned in single domains can be merged.
The model abstracts how agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human-AI systems.
This work advances multiagent reinforcement learning (MARL) for complex air combat by integrating methods that enhance decision-making and explainability. Using a realistic six-degrees-of-freedom aerial simulation built on the OpenAI Gymnasium framework, we investigate competitive agent interactions in dynamic scenario...
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