Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

  • Authors:

  • Meirav Hadad;Sarit Kraus;Yakov Gal;Raz Lin

  • Affiliations:

  • Department of Mathematics and Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel;Department of Mathematics and Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA;Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;Department of Mathematics and Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel

  • Venue:
  • Annals of Mathematics and Artificial Intelligence
  • Year:
  • 2003

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Abstract

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.