The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) Dependency Stochastic Boolean Satisfiability: A Logical Formalism for NEXPTIME Decision Problems with Uncertainty Nian-Ze Lee,1 Jie-Hong R. Jiang1, 2 1 Graduate Institute of Electronics Engineering, National Taiwan University 2 Department of Electrical Engineering, National Taiwan University No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan fd04943019,
[email protected] Abstract to their simplicity and generality, various satisfiability for- mulations are under active investigation. Stochastic Boolean Satisfiability (SSAT) is a logical formal- ism to model decision problems with uncertainty, such as Among the quantified decision procedures, QBF and Partially Observable Markov Decision Process (POMDP) for SSAT are closely related. While SSAT extends QBF to verification of probabilistic systems. SSAT, however, is lim- allow random quantifiers to model uncertainty, they are ited by its descriptive power within the PSPACE complex- both PSPACE-complete (Stockmeyer and Meyer 1973). A ity class. More complex problems, such as the NEXPTIME- number of SSAT solvers have been developed and applied complete Decentralized POMDP (Dec-POMDP), cannot be in probabilistic planning, formal verification of probabilis- succinctly encoded with SSAT. To provide a logical formal- tic design, partially observable Markov decision process ism of such problems, we extend the Dependency Quantified (POMDP), and analysis of software security. For example, Boolean Formula (DQBF), a representative problem in the solver MAXPLAN (Majercik and Littman 1998) encodes a NEXPTIME-complete class, to its stochastic variant, named conformant planning problem as an exist-random quanti- Dependency SSAT (DSSAT), and show that DSSAT is also ZANDER NEXPTIME-complete. We demonstrate the potential appli- fied SSAT formula; solver (Majercik and Littman cations of DSSAT to circuit synthesis of probabilistic and 2003) deals with partially observable probabilistic planning approximate design.