Modeling Probabilistic Commitments for Maintenance Is Inherently Harder than for Achievement

2020-06-02

Proceedings of the AAAI Conference on Artificial Intelligence, 34

Volume

Vol. 34 No. 06: AAAI-20 Technical Tracks 6

AAAI Technical Track: Reasoning under Uncertainty

Most research on probabilistic commitments focuses on commitments to achieve enabling preconditions for other agents. Our work reveals that probabilistic commitments to instead maintain preconditions for others are surprisingly harder to use well than their achievement counterparts, despite strong semantic similarities. We isolate the key difference as being not in how the commitment provider is constrained, but rather in how the commitment recipient can locally use the commitment specification to approximately model the provider's effects on the preconditions of interest. Our theoretic analyses show that we can more tightly bound the potential suboptimality due to approximate modeling for achievement than for maintenance commitments. We empirically evaluate alternative approximate modeling strategies, confirming that probabilistic maintenance commitments are qualitatively more challenging for the recipient to model well, and indicating the need for more detailed specifications that can sacrifice some of the agents' autonomy.

10.1609/aaai.v34i06.6596