Characterizing and Automatically Finding Primary Effects in Planning

Characterizing and Automatically Finding Primary Eeects in Planning

The use of primary eeects of operators in planning is an eeective approach to reduce search costs. However, the characterization of \good" primary eeects has remained at an informal level. In this paper we present a formal criterion for selecting useful primary eeects, which guarantees planning eeciency, completeness, and optimality. We also describe an inductive learning algorithm based on thi...

Automatically Abstracting the Effects of Operators

The use of abstraction in problem solving is an effective approach to reducing search, but finding good abstractions is a difficult problem. The first algorithm that completely automates the generation of abstraction hierarchies is Knoblock’s ALPINE, but this algorithm is only able to automatically abstract the preconditions of operators. In this paper we present an algorithm that automatically...

Characterizing and finding full dimensional efficient facets of PPS with constant returns to scale technology

Search Reduction in Planning with Primary Effects

The use of primary effects in planning is an effective approach for reducing search costs, closely related to abstraction planning. However, there has been little analysis of planning with primary effects and few experimental results. In this paper we demonstrate the connection between primary effects and abstraction hierarchies and provide analytical and empirical results on the efficiency of ...

Automatically Selecting and Using Primary Effects in Planning: Theory and Experiments

The use of primary effects of operators is an effective approach to improving the efficiency of planning. The characterization of “good” primary effects, however, has remained at an informal level and there have been no algorithms for selecting primary effects of operators. We formalize the use of primary effects in planning and present a criterion for selecting useful primary effects, which gu...