Multi-tier Automated Planning for Adaptive Behavior (Extended Version)
This work addresses the risk in planning modeling tasks for knowledge engineers, but it appears incremental as it builds on existing software engineering concepts.
The authors tackled the problem of incomplete planning domain models by proposing a multi-tier framework that allows specifying different sets of assumptions and objectives to support adaptive behavior, and they solved problem instances through a compilation to non-deterministic planning.
A planning domain, as any model, is never complete and inevitably makes assumptions on the environment's dynamic. By allowing the specification of just one domain model, the knowledge engineer is only able to make one set of assumptions, and to specify a single objective-goal. Borrowing from work in Software Engineering, we propose a multi-tier framework for planning that allows the specification of different sets of assumptions, and of different corresponding objectives. The framework aims to support the synthesis of adaptive behavior so as to mitigate the intrinsic risk in any planning modeling task. After defining the multi-tier planning task and its solution concept, we show how to solve problem instances by a succinct compilation to a form of non-deterministic planning. In doing so, our technique justifies the applicability of planning with both fair and unfair actions, and the need for more efforts in developing planning systems supporting dual fairness assumptions.