How can I predict opponent robot behavior without knowing their exact plans?
TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning
This paper introduces Task-Aware Behavior Fields (TAB-Fields), a method for predicting the likely actions of an adversary in a multi-agent system when their exact decision-making process is unknown, but their overall goals (like reaching specific locations) are. TAB-Fields work by calculating the probability distribution of the adversary's possible future states, constrained by their mission objectives and the environment, using the principle of maximum entropy.
For LLM-based multi-agent systems, TAB-Fields offer a way to represent and reason about the uncertainty of other agents' actions without needing to model their internal policies explicitly. This is particularly relevant when agents might use diverse and complex reasoning processes that are difficult to simulate directly or when data on their behavior is sparse. This could be used for planning, belief updates, and decision-making in scenarios where interacting with other LLM agents with possibly unknown inner workings is necessary. This work integrates TAB-Fields with a planning algorithm (POMCP) demonstrating its feasibility for real-time applications involving multiple LLM agents.