GoCo: Planning Expressive Commitment Protocols

GoCo: Planning Expressive Commitment Protocols

Meneguzzi, Felipe (Pontifical Catholic University of Rio Grande do Sul)
Magnaguagno, Mauricio C. (Pontifical Catholic University of Rio Grande do Sul)
Singh, Munindar P. (University of North Carolina)
Telang, Pankaj R. (SAS Institute Inc.)
Yorke-Smith, N. (TU Delft Algorithmics; American University of Beirut)

2018

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.

Commitment protocols
Goal reasoning
HTN planning
Intelligent agents
Non-determinism
Uncertainty

http://resolver.tudelft.nl/uuid:c4c5d445-f622-4c3d-be07-aece428c0340

https://doi.org/10.1007/s10458-018-9385-0

1387-2532

Autonomous Agents and Multi-Agent Systems, 32 (4), 459-502

Institutional Repository

journal article

© 2018 Felipe Meneguzzi, Mauricio C. Magnaguagno, Munindar P. Singh, Pankaj R. Telang, N. Yorke-Smith