TY - GEN
T1 - Let's learn their language? a case for planning with automata-network languages from model checking
AU - Hoffmann, Jörg
AU - Hermanns, Holger
AU - Klauck, Michaela
AU - Steinmetz, Marcel
AU - Karpas, Erez
AU - Magazzeni, Daniele
N1 - Publisher Copyright: © 2020, Association for the Advancement of Artificial Intelligence.
PY - 2020
Y1 - 2020
N2 - It is widely known that AI planning and model checking are closely related. Compilations have been devised between various pairs of language fragments. What has barely been voiced yet, though, is the idea to let go of one's own modeling language, and use one from the other area instead. We advocate that idea here - to use automata-network languages from model checking instead of PDDL - motivated by modeling difficulties relating to planning agents surrounded by exogenous agents in complex environments. One could, of course, address this by designing additional extended planning languages. But one can also leverage decades of work on modeling in the formal methods community, creating potential for deep synergy and integration with their techniques as a side effect. We believe there's a case to be made for the latter, as one modeling alternative in planning among others.
AB - It is widely known that AI planning and model checking are closely related. Compilations have been devised between various pairs of language fragments. What has barely been voiced yet, though, is the idea to let go of one's own modeling language, and use one from the other area instead. We advocate that idea here - to use automata-network languages from model checking instead of PDDL - motivated by modeling difficulties relating to planning agents surrounded by exogenous agents in complex environments. One could, of course, address this by designing additional extended planning languages. But one can also leverage decades of work on modeling in the formal methods community, creating potential for deep synergy and integration with their techniques as a side effect. We believe there's a case to be made for the latter, as one modeling alternative in planning among others.
UR - http://www.scopus.com/inward/record.url?scp=85091926960&partnerID=8YFLogxK
M3 - منشور من مؤتمر
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 13569
EP - 13575
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -