Commitment Semantics for Sequential Decision Making Under Reward Uncertainty

A commitment represents an agent's intention to attempt to bring about some state of the world that is desired by some agent (possibly itself) in the future. Thus, by making a commitment, an agent is agreeing to make sequential decisions that it believes can cause the desired state to arise. In general, though, an agent's actions will have uncertain outcomes, and thus reaching the desired state cannot be guaranteed. For such sequential decision settings with uncertainty, therefore, commitments can only be probabilistic. We argue that standard notions of commitment are insufficient for probabilistic commitments, and propose a new semantics that judges commitment fulfillment not in terms of whether the agent achieved the desired state, but rather in terms of whether the agent made sequential decisions that in expectation would have achieved the desired state with (at least) the promised probability. We have devised various algorithms that operationalize our semantics, to capture problem contexts with probabilistic commitments arising because action outcomes are uncertain, as well as arising because an agent might realize over time that it does not want to fulfill the commitment.