There are a lot of approaches for solving planning problems. Many of these approaches are based on `brute force` search methods and do not care about structures of plans previousl...
The automated planning community has traditionally focused on the efficient synthesis of plans given a complete domain theory. In the past several years, this line of work met wi...
Despite the long history of classical planning, there has been very little comparative analysis of the performance tradeoffs offered by the multitude of existing planning algorith...
Planning by analogical reasoning is a learning method that consists of the storage, retrieval, and replay of planning episodes. Planning performance improves with the accumulationa...
Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally...
A possibilistic approach of planning under uncertainty has been developed recently. It applies to problems in which the initial state is partially known and the actions have graded...
In this paper we describe SINERGY, which is a highly parallelizable, linear planning system that is based on the genetic programming paradigm. Rather than reasoning about the world...
We have been developing Rogue, an architecture that integrates high-level planning with a low-level executing robotic agent. Rogue is designed as the oce gofer task planner for X...
The initiative in STRIPS planning has recently been taken by work on propositional satisfiability. Best current planners, like Graphplan, and earlier planners originating in the p...
Signi cant advances have been made in the area of macro planning for assembly operations i.e., dividing a product into sub-assemblies, determining the sequence of assembly operati...
S. K. Gupta, Christiaan J. J. Paredis, P. F. Brown