The Trains project is an effort to build a conversationally proficient planning assistant. A key part of the project is the construction of the Trains system, which provides the research platform for a wide range of issues in natural language understanding, mixedinitiative planning systems, and representing and reasoning about time, actions and events. Four years have now passed since the beginning of the project. Each year we have produced a demonstration system that focused on a dialog that illustrates particular aspects of our research. The commitment to building complete integrated systems is a significant overhead on the research, but we feel it is essential to guarantee that the results constitute real progress in the field. This paper describes the goals of the project, and our experience with the effort so far. This paper is to appear in the Journal of Experimental and Theoretical AI, 1995. The TRAINS project has been funded in part by ONR/ARPA grant N00014-92-J-1512, U.S. Air ...

Reasoning about temporal information is an important task in many areas of Artificial Intelligence. In this paper we address the problem of scalability in temporal reasoning by providing a collection of new algorithms for efficiently managing large sets of qualitative temporal relations. We focus on the class of relations forming the Point Algebra (PA-relations) and on a major extension to include binary disjunctions of PA-relations (PA-disjunctions). Such disjunctions add a great deal of expressive power, including the ability to stipulate disjointness of temporal intervals, which is important in planning applications. Our representation of time is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. The approach is an extension of the time representation proposed by Schubert, Taugher and Miller in the context of story comprehension. The algorithms herein enable construction of a timegraph from a given set of PA-r...

We describe some simple domain-independent improvements to plan-refinement strategies for well-founded partial order planning that promise to bring this style of planning closer to practicality. One suggestion concerns the strategy for selecting plans for refinement among the current (incomplete) candidate plans. We propose an A* heuristic that counts only steps and open conditions, while ignoring "unsafe conditions" (threats). A second suggestion concerns the strategy for selecting open conditions (goals) to be established next in a selected incomplete plan. Here we propose a variant of a strategy suggested by Peot & Smith and studied by Joslin & Pollack; the variant gives top priority to unmatchable open conditions (enabling the elimination of the plan), secondhighest priority to goals that can only be achieved uniquely, and otherwise uses LIFO prioritization. The preference for uniquely achievable goals is a "zerocommitment " strategy in the sense that the corresponding plan refinem...

We address the problems of determining consistency and of finding a solution for sets of 3-point relations expressing exclusion of a point from an interval, and for sets of 4-point relations expressing interval disjointness. Availability of these relations is an important requirement for dealing with the sorts of temporal constraints encountered in many AI applications such as plan reasoning. We prove that consistency testing is NP-complete and finding a solution is NP-hard. Keywords: temporal reasoning, complexity of reasoning, planning, reasoning with disjunctions The work of the first author was carried out in part during a visit at the Computer Science Department of the University of Rochester (NY) supported by the Italian National Research Council (CNR), and in part at IRST in the context of the MAIA project and CNR projects "Sistemi Informatici e Calcolo Parallelo" and "Pianificazione Automatica". The second author was supported by Rome Lab Contract F30602-91-C-0010. 1 Introd...

We describe two domain-independent temporal reasoning systems called TimeGraph I and II which can be used in AI-applications as tools for efficiently managing large sets of relations in the Point Algebra, in the Interval Algebra, and metric information such as absolute times and durations. Our representation of time is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. TimeGraph I was originally developed by Taugher, Schubert and Miller in the context of story comprehension. TimeGraph II provides useful extensions, including efficient algorithms for handing inequations, and relations expressing point-interval exclusion and interval disjointness. These extensions make the system much more expressive in the representation of qualitative information and suitable for a large class of applications. Keywords: Temporal reasoning systems, Point algebra, Interval Algebra, Scalable systems 1 1 Introduction We ...