Addendum to: Summary Information for Reasoning About Hierarchical Plans
This work addresses a crucial step for hierarchical planning in AI, enabling richer classical plans with abstract actions, but it appears incremental as it builds on existing approaches with more conventional hierarchies.
The paper tackles the problem of deriving precondition and effect summaries from hierarchical agent plans, presenting formal definitions, data structures, and algorithms to automate this process for more pragmatic hierarchies than previously addressed.
Hierarchically structured agent plans are important for efficient planning and acting, and they also serve (among other things) to produce "richer" classical plans, composed not just of a sequence of primitive actions, but also "abstract" ones representing the supplied hierarchies. A crucial step for this and other approaches is deriving precondition and effect "summaries" from a given plan hierarchy. This paper provides mechanisms to do this for more pragmatic and conventional hierarchies than in the past. To this end, we formally define the notion of a precondition and an effect for a hierarchical plan; we present data structures and algorithms for automatically deriving this information; and we analyse the properties of the presented algorithms. We conclude the paper by detailing how our algorithms may be used together with a classical planner in order to obtain abstract plans.