When rational, utility-maximizing agents encounter an opportunity to collaborate on a group activity, they must determine whether to commit to that activity. We refer to this problem as the initial-commitment decision problem (ICDP

Morris, Muscettola and Vidal (MMV) presented an algorithm for checking the dynamic controllability (DC) of temporal networks in which certain temporal durations are beyond the control of the planning agent. Their DC-checking algorithm is based on rules for inferring new constraints based on the real-time context within which execution decisions must be made. This paper presents a counter-example to demonstrate that some of the inference rules are, in fact, not sound. The paper fixes the problem by strengthening the definition of dynamic execution strategies to correctly capture the central prohibition against advance knowledge of future events. The new definition enables MMV's soundness proof to go through with minimal changes. It then uses the stronger definition to derive an equivalent, alternative characterization of dynamic execution strategies that highlights the real-time execution decisions that a planning agent must make. The procedural strategy used by MMV in their completeness proof is shown to satisfy the stronger definition, thus ensuring that the DC-checking algorithm is also complete with respect to the stronger definition. As a result, the paper puts MMV's DC-checking algorithm on a more solid theoretical foundation, while also providing a more practical characterization of dynamic execution strategies.

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An adequate formulation of collective intentionality is crucial for understanding group activity and for modeling the mental state of participants in such activities. Although work on collective intentionality in philosophy, artificial intelligence, and cognitive science has many points of agreement, several key issues remain under debate. This paper argues that the dynamics of intention-in particular, the interrelated processes of plan-related group decision making and intention updatingplay crucial roles in an explanation of collective intentionality. Furthermore, it is in these dynamic aspects that coordinated group activity differs most from individual activity. The paper specifies a model of the dynamics of agent intentions in the context of collaborative activity. Its integrated treatment of group decision making and coordinated updating of group-related intentions fills an important gap in prior accounts of collective intentionality, thus helping to resolve a long-standing debate about the nature of intentions in group activity. The paper also defines an architecture for collaboration-capable computer agents that satisfies the constraints of the model and is a natural extension of the standard architecture for resource-bounded agents operating as individuals. The new architecture is both more principled and more complete than prior architectures for collaborative multi-agent systems.

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