Specification and Implementation of a Belief-Desire-Joint-Intention Architecture for Collaborative Problem Solving

Abstract

Systems composed of multiple interacting problem solvers are becoming increasingly pervasive and have been championed in some quarters as the basis of the next generation of intelligent information systems. If this technology is to fulfill its true potential then it is important that the systems which are developed have a sound theoretical grounding. One aspect of this foundation, namely the model of collaborative problem solving, is examined in this paper. A synergistic review of existing models of cooperation is presented, their weaknesses are highlighted and a new model (called joint responsibility) is introduced. Joint responsibility is then used to specify a novel high-level agent architecture for cooperative problem solving in which the mentalistic notions of belief, desire, intention and joint intention play a central role in guiding an individual's and the group's problem solving behaviour. An implementation of this highlevel architecture is then discussed and its utility is illustrated for the real-world domain of electricity transportation management.

Safety in the Context of Coordination via Adjustable Autonomy

“…The current version of the code, known as Machinetta, is a substantially extended and updated version of the TEAMCORE proxy code [19]. TEAMCORE proxies implement teamwork as described by the STEAM algorithms [8], which are in in turn based on the theory of joint intentions [21,22].…”

Towards Flexible Coordination of Large Scale Multi-Agent Teams

“…Team members can determine which sub-goals are relevant by the plan templates specified in the library. Recipe i is a description of the way the sub-goal will be achieved [11] including the execution order of the components in the plan. Roles i = {r 1 , r 2 , r 3 , ...r r } are the individual activities that must be performed to execute recipe i .…”

“…According to such research, there are several parameters that are important for helping us to understand or predict the behavior of information sharing in large-scale teams. Key factors include the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks [11]. Most of them are interrelated.…”

“…Team members use these models to reason about actions that will aid the achievement of joint goals [11,28]. However, when the size of a team is scaled up, it becomes unfeasible to maintain up-to-date, detailed models of all other teammates, or even of all team activities.…”

“…Without these models, key elements of both the theory and operationalization of teamwork break down. For example, without accurate models of team activities, STEAM's communication reasoning [28] cannot be applied, nor can Joint Intention's reasoning about committments [11].…”