Architectures for Agents that Track Other Agents in Multi-Agent Worlds

In multi-agent environments, an intelligent agent often needs to interact with other individuals or groups of agents to achieve its goals. Agent tracking is one key capability required for intelligent interaction. It involves monitoring the observable actions of other agents and inferring their unobserved actions, plans, goals and behaviors. This article examines the implications of such an agent tracking capability for agent architectures. It specifically focuses on real-time and dynamic environments, where an intelligent agent is faced with the challenge of tracking the highly flexible mix of goal-driven and reactive behaviors of other agents, in real-time. The key implication is that an agent architecture needs to provide direct support for flexible and efficient reasoning about other agents' models. In this article, such support takes the form of an architectural capability to execute the other agent's models, enabling mental simulation of their behaviors. Other architectural requirements that follow include the capabilities for (pseudo-) simultaneous execution of multiple agent models, dynamic sharing and unsharing of multiple agent models and high bandwidth inter-model communication. We have implemented an agent architecture, an experimental variant of the Soar integrated architecture, that conforms to all of these requirements. Agents based on this architecture have been implemented to execute two different tasks in a real-time, dynamic, multi-agent domain. The article presents experimental results illustrating the agents' dynamic behavior.