Factored Beliefs for Machine Agents in Decentralized Partially Observable Markov Decision Processes

Joshua Lapso
Gilbert L. Peterson, Air Force Institute of Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY 4)


A shared mental model (SMM) is a foundational structure in high performing, task-oriented teams and aid humans in determining their teammate's goals and intentions. Higher levels of mental alignment between teammates can reduce the direct dialogue required for team success. For decision-making teams, a transactive memory system (TMS) offers team members a map of specialized knowledge, indicating source of knowledge and the source's credibility. SMM and TMS formulations aid human-agent team performance in their intended team types. However, neither improve team performance with a project team--one that requires both behavioral and knowledge integration. We present a hybrid cognitive model (HCM) for machine agents that subsumes the integrated portions of a team's transactive memory in an SMM. The unified structure of the HCM enables contextual switches during execution for machine agents, over the two cognitive formulations with comparable computational complexity of a single cognitive model. Results in a multi-agent project environment demonstrates how the HCM provides machine agents with a generalizable cognitive structure that is able to maintain fully factored belief states with minimal inter-agent communication.