Group planning with time constraints

  • Meirav Hadad
  • , Sarit Kraus
  • , Irith Ben-Arroyo Hartman
  • , Avi Rosenfeld

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Embedding planning systems in real-world domains has led to the necessity of Distributed Continual Planning (DCP) systems where planning activities are distributed across multiple agents and plan generation may occur concurrently with plan execution. A key challenge in DCP systems is how to coordinate activities for a group of planning agents. This problem is compounded when these agents are situated in a real-world dynamic domain where the agents often encounter differing, incomplete, and possibly inconsistent views of their environment. To date, DCP systems have only focused on cases where agents' behavior is designed to optimize a global plan. In contrast, this paper presents a temporal reasoning mechanism for self-interested planning agents. To do so, we model agents' behavior based on the Belief-Desire-Intention (BDI) theoretical model of cooperation, while modeling dynamic joint plans with group time constraints through creating hierarchical abstraction plans integrated with temporal constraints network. The contribution of this paper is threefold: (i) the BDI model specifies a behavior for self interested agents working in a group, permitting an individual agent to schedule its activities in an autonomous fashion, while taking into consideration temporal constraints of its group members; (ii) abstract plans allow the group to plan a joint action without explicitly describing all possible states in advance, making it possible to reduce the number of states which need to be considered in a BDI-based approach; and (iii) a temporal constraints network enables each agent to reason by itself about the best time for scheduling activities, making it possible to reduce coordination messages among a group. The mechanism ensures temporal consistency of a cooperative plan, enables the interleaving of planning and execution at both individual and group levels. We report on how the mechanism was implemented within a commercial training and simulation application, and present empirical evidence of its effectiveness in real-life scenarios and in reducing communication to coordinate group members' activities.

Original languageEnglish
Pages (from-to)243-291
Number of pages49
JournalAnnals of Mathematics and Artificial Intelligence
Volume69
Issue number3
DOIs
StatePublished - Nov 2013

Bibliographical note

Funding Information:
This work was supported in part by ERC grant number 267523, the Google Inter-University Center for Electronic Markets and Auctions and MURI grant number W911NF-08-1-0144. Preliminary results appeared in CIA-01 [30] and in CIA-02 [31].

Funding

This work was supported in part by ERC grant number 267523, the Google Inter-University Center for Electronic Markets and Auctions and MURI grant number W911NF-08-1-0144. Preliminary results appeared in CIA-01 [30] and in CIA-02 [31].

FundersFunder number
Google Inter-university center for Electronic Markets and Auctions
Seventh Framework Programme267523
Multidisciplinary University Research InitiativeW911NF-08-1-0144
European Commission

    Keywords

    • Artificial intelligence
    • Cooperation
    • Coordination
    • Multiagent system
    • Planning
    • Time constraints

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