This article addresses the challenge of planning coordinated activities for a set of autonomous agents, who coordinate according to social commitments among themselves. We develop a multi-agent plan in the form of a commitment protocol that allows the agents to coordinate in a flexible manner, retaining their autonomy in terms of the goals they adopt so long as their actions adhere to the commitments they have made. We consider an expressive first-order setting with probabilistic uncertainty over action outcomes. We contribute the first practical means to derive protocol enactments which maximise expected utility from the point of view of one agent. Our work makes two main contributions. First, we show how Hierarchical Task Network planning can be used to enact a previous semantics for commitment and goal alignment, and we extend that semantics in order to enact first-order commitment protocols. Second, supposing a cooperative setting, we introduce uncertainty in order to capture the reality that an agent does not know for certain that its partners will successfully act on their part of the commitment protocol. Altogether, we employ hierarchical planning techniques to check whether a commitment protocol can be enacted efficiently, and generate protocol enactments under a variety of conditions. The resulting protocol enactments can be optimised either for the expected reward or the probability of a successful execution of the protocol. We illustrate our approach on a real-world healthcare scenario.
We gratefully thank those who shared their code with us. Special thanks to Ugur Kuter. We thank the anonymous reviewers, and also acknowledge with gratitude the reviewers at ProMAS’11, AAMAS’13, AAAI’13, and AAMAS’15, where preliminary parts of this work appeared. FM thanks the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the support within process numbers 306864/2013-4 under the PQ fellowship and 482156/2013-9 under the Universal project programs. NYS acknowledges support of the AUB University Research Board Grant Number 102853 and the OSB Grant OFFER_C1_2013_2014.