Improved decisions for unknown behaviours in interactive dynamic influence diagrams

Interactive dynamic influence diagrams (I-DIDs) are a general decision framework for a subject agent who interacts with other agents (of either collaborative or competitive) in a common environment with partial observability. The subject agent aims to optimize its decision-making (response strategy) while other agents concurrently adapt their behaviors over time. The I-DID model has faced a long-term challenge when other agents exhibit unknown behaviors that go beyond what the subject agent has planned for prior to their interactions. This is because the subject agent does not hold the capability of modeling unknown behaviours of other agents in traditional I-DID techniques. In this article, we adapt two different swarm intelligence (SI) techniques to develop new behaviours for other agents in I-DIDs. The SI-based algorithms have the strength of generating a collective set of behaviours that could potentially contain various types of agents’ behaviours. We theoretically analyze how the two algorithms impact the subject agent’s decision quality, and empirically demonstrate the algorithm performance in two commonly used problem domains.