2026 AAAI AAAI 2026

Theory-of-Mind in Partially Observed, Mixed-Motive Games

Abstract

Abstract Theory of Mind (ToM) enables agents to model others' mental states, but in mixed-motive games, this capacity can lead to deceptive behaviour and alignment risks. My research investigates how ToM affects strategic behaviour in partially observed games, contributing: (1) a formal model of ToM-driven manipulation in a preference elicitation task, (2) evidence that excessive ToM leads to paranoid-like overmentalisation, and (3) the Aleph-IPOMDP model, a framework for multi-agent systems that balances ToM reasoning with game-theoretic principles to prevent manipulation, deterring capable agents from deceiving. My work contributes to the understanding of deceptive AI, overcoming deception in multi-agent systems and applications to computational model of human cognition.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors