Semantics and algorithms for trustworthy commitment achievement under model uncertainty
| Autor: | Edmund H. Durfee, Satinder Singh, Qi Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Semantics (computer science) Computation media_common.quotation_subject Formal semantics (linguistics) 05 social sciences Autonomous agent Probabilistic logic 02 engineering and technology Semantics Artificial Intelligence Semantics of logic 0502 economics and business 0202 electrical engineering electronic engineering information engineering Dependability 050206 economic theory 020201 artificial intelligence & image processing Quality (business) Algorithm Autonomy media_common |
| Zdroj: | Autonomous Agents and Multi-Agent Systems. 34 |
| ISSN: | 1573-7454 1387-2532 |
| DOI: | 10.1007/s10458-020-09443-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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