Semantics and algorithms for trustworthy commitment achievement under model uncertainty

We focus on how an agent can exercise autonomy while still dependably fulfilling commitments it has made to another, despite uncertainty about outcomes of its actions and how its own objectives might evolve. Our formal semantics treats a probabilistic commitment as constraints on the actions an autonomous agent can take, rather than as promises about states of the environment it will achieve. We have developed a family of commitment-constrained (iterative) lookahead algorithms that provably respect the semantics, and that support different tradeoffs between computation and plan quality. Our empirical results confirm that our algorithms’ ability to balance (selfish) autonomy and (unselfish) dependability outperforms optimizing either alone, that our algorithms can effectively handle uncertainty about both what actions do and which states are rewarding, and that our algorithms can solve more computationally-demanding problems through judicious parameter choices for how far our algorithms should lookahead and how often they should iterate.