RealPlan: Decoupling Causal and Resource Reasoning in Planning

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 17

Planning and Scheduling

Recent work has demonstrated that treating resource reasoning separately from causal reasoning can lead to improved planning performance and rational resource management where increase in resources does not degrade planning performance. However, the resources were scheduled procedurally and limited to cases that could be solved backtrack-free. Terming the decoupled framework as RealPlan, in this work, I extend it with a general approach to convert the resource allocation problem as a declaratively specified dynamic constraint satisfaction problem (DCSP), compile it into CSP and solve it with a CSP solver. By doing so, the resource scheduling problem can be handled in its full complexity and can provide a computational characterization of the different scheduling classes. The CSP formulation also facilitates planner-scheduler interaction by helping the scheduler interpret the resource allocation policies proposed by the planner in terms of constraints on values of scheduling variables. Moreover, if the extraction of causal plan is also formulated as a CSP problem, the two CSPs can enable dependency directed backtracking between them. I have implemented declarative scheduling on top of Graphplan and GP-CSP planners (which poses the backward search of Graphplan as a CSP problem), and the resulting planners reiterate the benefits of decoupling planning and scheduling while providing elegant CSP models (RealPlan-MS, RealPlan-PP) for investigating planner-scheduler communication.