Multi-Agent Planning and Diagnosis with Commonsense Reasoning

Tran Cao Son, William Yeoh, Roni Stern, Meir Kalech

Abstract: In multi-agent systems, multi-agent planning and diagnosis are two key subfields – multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multi-agent diagnosis approaches identify root causes for faults when they occur, typically by using information from the multi-agent planning model as well as the resulting multi-agent plan. However, when a plan fails during execution, the cause can often be related to some commonsense information that is neither explicitly encoded in the planning nor diagnosis problems. As such existing diagnosis approaches fail to accurately identify the root causes in such situations. To remedy this limitation, we extend the Multi-Agent STRIPS problem (a common multi-agent planning framework) to a Commonsense Multi-Agent STRIPS model, which includes commonsense fluents and axioms that may affect the classical planning problem. We show that a solution to a (classical) Multi-Agent STRIPS problem is also a solution to the commonsense variant of the same problem. Then, we propose a decentralized multi-agent diagnosis algorithm, which uses the commonsense information to diagnose faults when they occur during execution. Finally, we demonstrate the feasibility and promise of this approach on several key multi-agent planning benchmarks.