Abstract
To build a theory of intention revision for agents operating in stochastic environments, we need a logic in which we can explicitly reason about their decision-making policies and those policies’ uncertain outcomes. Toward this end, we propose PLBP, a novel probabilistic temporal logic for Markov Decision Processes that allows us to reason about policies of bounded size. The logic is designed so that its expressive power is sufficient for the intended applications, whilst at the same time possessing strong computational properties. We prove that the satisfiability problem for our logic is decidable, and that its model checking problem is PSPACE-complete. This allows us to e.g. algorithmically verify whether an agent’s intentions are coherent, or whether a specific policy satisfies safety and/or liveness properties.
Original language | English |
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Title of host publication | Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence |
Subtitle of host publication | Main Track |
Editors | Edith Elkind |
Publisher | IJCAI |
Pages | 3296-3303 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-956792-03-4 |
DOIs | |
Publication status | Published - 19 Jun 2023 |
Event | IJCAI 2023: 32nd International Joint Conference on Artificial Intelligence - Macao, China Duration: 19 Jun 2023 → 25 Jun 2023 Conference number: 32 https://ijcai-23.org/ |
Conference
Conference | IJCAI 2023 |
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Country/Territory | China |
City | Macao |
Period | 19/06/23 → 25/06/23 |
Internet address |