Group planning with time constraints

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-DesireIntention (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.

Model-based approaches for technology planning and roadmapping: Technology forecasting and game-theoretic modeling

“…In Brafman et al (2009), a Coalition-Planning game formulation has been developed for self-interested players with personal goals who find the cooperation with each other beneficial in order to increase their personal net benefit. The research is focused on cooperative self-interested agents in groups (Hadad et al, 2013) and game scenarios in resource coalition (Dunne et al, 2010). A pure game-theoretic approach has been proposed to perform a strategic analysis of all possible player strategies and define equilibria based on the relationships between different solutions in game-theoretic terms (Bowling et al, 2003).…”

An Abstract Framework for Non-Cooperative Multi-Agent Planning

“…The system proposed in [17] couples coalition formation with planning by allocating the best possible team of robots for each task, thus reducing planning complexity. Authors in [18] presented a temporal reasoning mechanism for self-interested planning agents. In this work, agents' behavior is modeled on the basis of the Belief-Desire-Intention (BDI) theoretical model of cooperation to compute joint plans with time constraints.…”

A better-response strategy for self-interested planning agents

“…When agents that have their own incentives are involved in a MAP problem, there is a need for a stable solution plan, a plan from which none of the agents is willing to deviate during execution because otherwise it would only imply a loss of utility to some of them. In coalitional planning, self-interested agents create coalitions in order to share resources and cooperate on goal achievement because joining forces turns out to be more beneficial for reaching their goals [3,8,13]. Hence, in cooperative game-theoretic models such as coalitional planning, selfinterested agents build their plans on the basis of a cooperative behavior and exploitation of synergies with the other agents.…”