Heuristics for Planning, Plan Recognition and Parsing

TL;DR

This paper demonstrates that Plan Recognition over libraries, including CFGs, can be formulated as classical planning problems, revealing the strengths and limitations of current heuristics and suggesting directions for improvement.

Contribution

It generalizes Plan Recognition to include complex libraries like CFGs within a planning framework, unifying different recognition problems.

Findings

Planning heuristics effectively handle standard libraries

Recognition over complex libraries exposes heuristic limitations

Potential improvements for planning heuristics are identified

Abstract

In a recent paper, we have shown that Plan Recognition over STRIPS can be formulated and solved using Classical Planning heuristics and algorithms. In this work, we show that this formulation subsumes the standard formulation of Plan Recognition over libraries through a compilation of libraries into STRIPS theories. The libraries correspond to AND/OR graphs that may be cyclic and where children of AND nodes may be partially ordered. These libraries include Context-Free Grammars as a special case, where the Plan Recognition problem becomes a parsing with missing tokens problem. Plan Recognition over the standard libraries become Planning problems that can be easily solved by any modern planner, while recognition over more complex libraries, including Context-Free Grammars (CFGs), illustrate limitations of current Planning heuristics and suggest improvements that may be relevant in other Planning problems too.