Planning as Propositional CSP: From Walksat to Local Search Techniques for Action Graphs

摘要

Graphplan-style of planning can be formulated as an incremental propositional CSP where the (boolean) variables correspond to operator instantiations (actions) that are or are not scheduled at certain time steps. In this paper we present a framework for solving this class of propositional CSPs using local search in planning graphs. The search space consists of particular subgraphs of a planning graph corresponding to (complete) variable assignments, and representing partial plans. The operators for moving from one search state to the next one are graph modifications corresponding to revisions of the current variable assignment (partial plan), or to an extension of the represented CSP. Our techniques are implemented in a planner called LPG using various types of heuristics based on a parametrized objective function, where the parameters weight different constraint violations, and are dynamically evaluated using Lagrange multipliers. LPG's basic heuristic was inspired by Walksat, which in Kautz and Selman's Blackbox can be used to solve the SAT-encoding of a planning graph. An advantage of LPG is that its heuristics exploit the structure of the planning graph, while Blackbox relies on general heuristics for SAT-problems, and requires the translation of the planning graph into propositional clauses. Another major difference is that LPG can handle action execution costs to produce good quality plans. This is achieved by an "anytime" process minimizing an objective function based on the number of constraint violations in a plan and on its overall cost. Experimental results illustrate the efficiency of our approach, showing, in particular, that LPG is significantly faster than Blackbox and other planners based on planning graphs.