Forward-Chaining Partial-Order Planning

Over the last few years there has been a revival of interest in the idea of least-commitment planning with a number of researchers returning to the partial-order planning approaches of UCPOP and VHPOP. In this paper we explore the potential of a forward-chaining state-based search strategy to support partial-order planning in the solution of temporal-numeric problems. Our planner, POPF, is built on the foundations of grounded forward search, in combination with linear programming to handle continuous linear numeric change. To achieve a partial ordering we delay commitment to ordering decisions, timestamps and the values of numeric parameters, managing sets of constraints as actions are started and ended. In the context of a partially ordered collection of actions, constructing the linear program is complicated and we propose an efficient method for achieving this. Our late-commitment approach achieves flexibility, while benefiting from the informative search control of forward plannin...

• Type: Conference
• Year: 2010
• Where: AIPS
• Authors: Amanda Jane Coles, Andrew Coles, Maria Fox, Derek Long
• Keywords: AIPS 2010, Artificial Intelligence, Forward-chaining State-based Search, Partial-order Planning Approaches, Planning