Generating Replanning Goals through Multi-objective Optimization in Response to Execution Observation

In some applications, planning-monitoring systems generate plans and monitor their execution by other agents. During execution, agents might deviate from these plans for various reasons. The deviation from the expected behavior will be observed by the planning-monitoring system, which will replan in order to provide the agent a new suggested plan. Most existing replanning approaches maintain the goals and compute a plan that achieves them under the new circumstances. This is often not realistic, as achieving the original goal might be very costly or impossible under the new circumstances. Furthermore, replanning approaches usually overlook agent’s behavior up to the observed deviation from the original plan. In this paper we introduce GREPLAN, a novel approach that proposes new replanning goals (and plans) by solving a multi-objective optimization problem that considers all goals within a perimeter of the original goal. Empirical results in several planning benchmarks show that GREPLAN successfully reacts to deviations from the original plan by generating new appropriate replanning goals.