Plan Recovery Process in Multi-agent Dynamic Environments

Planning is the process that focuses on the choice and organization of actions through their expected effects. Plans can be affected by unexpected, uncontrolled, non-deterministic events leading to failures. Such challenging problem boosted works focusing agent distribution, communication mechanisms, privacy, among other issues. Nevertheless, the plan recovery process does not have a defined standard solution. Thus, in this work, we present a three-phase plan recovery process to provide resilience to agent plans by supporting a staggered solution. Whenever an action execution fails, agents try to solve individually through their own capabilities. But when not possible, agents start an interaction protocol to ask for help. Finally, when previous two phases were unsuccessful, a centralized planning process is trigged. Regardless the phase in which the solution is found, agents' plans are coordinated to guarantee cooperation maintaining information privacy. An empirical analysis applying metrics such as planning time, final plan length and message exchange was conducted. Results give statistical significant evidence that agents' autonomy is better explored in agents' loosely coupled environments. The contributions of this work include: a three-phase plan recovery process, a simulation tool for benchmarks, and a statistical robust evaluation method to multi-agent planning.