Improving AI planning by using extensible components

Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption.

To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.