Risk-aware Analysis for Interpretations of Probabilistic Achievement and Maintenance Commitments

Probabilistic commitments provide a computational framework for multi-agent coordination, where one autonomous agent (the commitment provider), commits to a future course of action that probabilistically influences the local state of another agent (the commitment recipient) in ways that the recipient desires. Conventionally, a probabilistic commitment is specified abstractly so as to give the provider latitude at run time about how to achieve it. Unfortunately, as we analyze in this article, this abstraction incurs a risk of suboptimal performance for the recipient. For (achievement) commitments by the provider to achieve conditions that the recipient prefers but that do not initially hold, we prove that the recipient can make modeling choices that bound its risk of suboptimality. Somewhat surprisingly, however, for (maintenance) commitments by the provider to maintain conditions whose initial values are already ones the recipient prefers, we prove that no such bounds on suboptimality risk are possible. We study the two types of commitments empirically to measure the suboptimality they incur under different conditions, and based on our theoretical and empirical results suggest that adding selective details when specifying probabilistic maintenance commitments can be beneficial.