TY - GEN
T1 - Automatically Verifying Expressive Epistemic Properties of Programs
AU - Belardinelli, Francesco
AU - Boureanu, Ioana
AU - Malvone, Vadim
AU - Rajaona, Fortunat
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - We propose a new approach to the verification of epistemic properties of programs. First, we introduce the new “program-epistemic” logic LPK, which is strictly richer and more general than similar formalisms appearing in the literature. To solve the verification problem in an efficient way, we introduce a translation from our language LPK into first-order logic. Then, we show and prove correct a reduction from the model checking problem for program-epistemic formulas to the satisfiability of their first-order translation. Both our logic and our translation can handle richer specification w.r.t. the state of the art, allowing us to express the knowledge of agents about facts pertaining to programs (i.e., agents' knowledge before and after a program is executed). Furthermore, we implement our translation in Haskell in a general way (i.e., independently of the programs in the logical statements), and we use existing SMT-solvers to check satisfaction of LPK formulas on a benchmark example in the AI/agency field.
AB - We propose a new approach to the verification of epistemic properties of programs. First, we introduce the new “program-epistemic” logic LPK, which is strictly richer and more general than similar formalisms appearing in the literature. To solve the verification problem in an efficient way, we introduce a translation from our language LPK into first-order logic. Then, we show and prove correct a reduction from the model checking problem for program-epistemic formulas to the satisfiability of their first-order translation. Both our logic and our translation can handle richer specification w.r.t. the state of the art, allowing us to express the knowledge of agents about facts pertaining to programs (i.e., agents' knowledge before and after a program is executed). Furthermore, we implement our translation in Haskell in a general way (i.e., independently of the programs in the logical statements), and we use existing SMT-solvers to check satisfaction of LPK formulas on a benchmark example in the AI/agency field.
U2 - 10.1609/aaai.v37i5.25769
DO - 10.1609/aaai.v37i5.25769
M3 - Conference contribution
AN - SCOPUS:85167874068
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 6245
EP - 6252
BT - AAAI-23 Technical Tracks 5
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
PB - AAAI Press
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Y2 - 7 February 2023 through 14 February 2023
ER -