We propose PLANL, an approach for PLAnning with Natural Language, to accelerate the development of automated planning systems, enable plan sharing across multiple planners, and facilitate natural language interaction. PLANL uses generative sublanguage ontologies (GSOs) to robustly and accurately translate planning knowledge descriptions into representations such as STRIPS or hierarchical task networks. GSO's accomplish this through a novel ability for efficiently representing and resolving polysemy. Unlike alternative approaches, PLANL does not have a proprietary plan representation. Instead, it exploits existing plan representations and selects a linguistically motivated conceptual vocabulary for them.
This analysis step extracts a set of planning goals as well as a set of geometrical and technological constraints, e.g. the technological requirement that a groove can only be machined if the outline which it is contained in, has been already machined. These constraints are represented by ordering relations between goals representing the planning problem (Figure 1). In classical planning, orderings are only defined between steps. In this sense the orderings between goals are used to imply orderings between the subplans to reach these goals. The effect of these orderings is that the size of the search space can be significantly reduced without any further reasoning or representation effort, because processing of these constraints is completely done by the analysis step and by internal consistency mechanisms of the planner.
This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems. The format of the conference included paper presentations, invited speakers, panel discussions, workshops, and a planning competition. The conference was cochaired by Steve Chien of the Jet Propulsion Laboratory (JPL) at the California Institute of Technology, Subbarao Kambhampati of Arizona State University, and Craig Knoblock of the University of Southern California Information Sciences Institute, with the proceedings published by AAAI Press (Chien, Kambhampati, and Knoblock 2000). The three workshops were "Analyzing and Exploiting Domain Knowledge for Efficient Planning," chaired by Maria Fox from University of Durham; "Decision-Theoretic Planning," chaired by Richard Goodwin from IBM's T. J. Watson Research Center and Sven Koenig from Georgia Institute of Technology; and "Model-Theoretic Approaches to Planning" by Paolo Traverso from The invited speakers at the conference presented some of their latest research and ideas on intelligent planning and execution: Drew McDermott from Yale University gave the first talk, entitled "Bottom-Up Knowledge Representation," and David Smith from The Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS2000) was held on 14-17 April 2000 at Breckenridge, Colorado; it was colocated with the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000). This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems.