King's College London

Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents' minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability.
However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents.
In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty.
To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.