Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
This work addresses a real-world planning problem for oil spill response, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the optimization of oil spill emergency response planning by combining a generative planner with an influence diagram solver, resulting in a simplified solution complexity that found an optimum solution equivalent to evaluating multiple plans simultaneously.
This paper works through the optimization of a real world planning problem, with a combination of a generative planning tool and an influence diagram solver. The problem is taken from an existing application in the domain of oil spill emergency response. The planning agent manages constraints that order sets of feasible equipment employment actions. This is mapped at an intermediate level of abstraction onto an influence diagram. In addition, the planner can apply a surveillance operator that determines observability of the state---the unknown trajectory of the oil. The uncertain world state and the objective function properties are part of the influence diagram structure, but not represented in the planning agent domain. By exploiting this structure under the constraints generated by the planning agent, the influence diagram solution complexity simplifies considerably, and an optimum solution to the employment problem based on the objective function is found. Finding this optimum is equivalent to the simultaneous evaluation of a range of plans. This result is an example of bounded optimality, within the limitations of this hybrid generative planner and influence diagram architecture.