An abstract knowledge based approach to diagnosis and recovery of plan failure in multi-agent systems
Open multi-agent systems (MAS) are decentralised and distributed systems that consist of a large number of loosely coupled autonomous agents. In the absence of centralised control they tend to be difficult to manage, especially in an open environment, which is dynamic, complex, distributed and unpredictable. This dynamism and uncertainty in an open environment gives rise to unexpected plan failures. In this paper we present an abstract knowledge based approach for the diagnosis and recovery of plan action failures. Our approach associates a sentinel agent with each problem solving agent in order to monitor the problem solving agents interactions. The proposed approach also requires the problem solving agents to be able to report on the status of a plans actions. Once an exception is detected the sentinel agents start an investigation of the suspected agents. The sentinel agents collect information about the status of failed plan abstract actions and knowledge about agents mental attitudes regarding any failed plan. The sentinel agent then uses this abstract knowledge and the agents mental attitudes, to diagnose the underlying cause of the plan failure. The sentinel agent may ask the problem solving agent to retry their failed plan based on the diagnostic result.
plan failure multi-agent systems exception diagnosis failure recovery intelligent agent distributed ai