We consider an agent that operates with two models of the environment: one that captures expected behaviors and one that captures additional exceptional behaviors. We study the problem of synthesizing agent strategies that enforce a goal against environments operating as expected while also making a best effort against exceptional environment behaviors. We formalize these concepts in the context of linear-temporal logic, and give an algorithm for solving this problem. We also show that there is no trade-off between enforcing the goal under the expected environment specification and making a best-effort for it under the exceptional one.
Synthesizing strategies under expected and exceptional environment behaviors / Aminof, B.; de Giacomo, G.; Lomuscio, A.; Murano, A.; Rubin, S.. - In: IJCAI. - ISSN 1045-0823. - 2021-:(2020), pp. 1674-1680. (Intervento presentato al convegno 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 nel 2021) [10.24963/ijcai.2020/232].
Synthesizing strategies under expected and exceptional environment behaviors
Murano A.
;Rubin S.
2020
Abstract
We consider an agent that operates with two models of the environment: one that captures expected behaviors and one that captures additional exceptional behaviors. We study the problem of synthesizing agent strategies that enforce a goal against environments operating as expected while also making a best effort against exceptional environment behaviors. We formalize these concepts in the context of linear-temporal logic, and give an algorithm for solving this problem. We also show that there is no trade-off between enforcing the goal under the expected environment specification and making a best-effort for it under the exceptional one.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
