An Architecture for Novelty Handling in a Multi-Agent Stochastic Environment: Case Study in Open-World Monopoly

The ability of AI agents and architectures to detect and adapt to sudden changes in their environments remains an outstanding challenge. In the context of multi-agent games, the agent may face novel situations where the rules of the game, the available actions, the environment dynamics, the behavior of other agents, as well as the agent’s goals suddenly change. In this paper, we introduce an architecture that allows agents to detect novelties, characterize those novelties, and build an appropriate adaptive model to accommodate them. Our agent utilizes logic and reasoning (specifically, Answer Set Programming) to characterize novelties into different categories, as to enable the agent to adapt to the novelty while maintaining high performance in the game. We demonstrate the effectiveness of the proposed agent architecture in a multi-agent imperfect information board game, Monopoly. We measure the success of the architecture by comparing our method to heuristics, and vanilla Monte-Carlo Tree Search approaches. Our results indicate precise novelty detection, and significant improvements in the performance of agents utilizing the novelty handling architecture.