Finite-State Abstractions for Probabilistic Computation Tree Logic

Probabilistic Computation Tree Logic (PCTL) is the established temporal logic for probabilistic verification of discrete-time Markov chains. Probabilistic model checking is a technique that verifies or refutes whether a property specified in this logic holds in a Markov chain. But Markov chains are often infinite or too large for this technique to apply. A standard solution to this problem is to convert the Markov chain to an abstract model and to model check that abstract model. The problem this thesis therefore studies is whether or when such finite abstractions of Markov chains for model checking PCTL exist. This thesis makes the following contributions. We identify a sizeable fragment of PCTL for which 3-valued Markov chains can serve as finite abstractions; this fragment is maximal for those abstractions and subsumes many practically relevant specifications including, e.g., reachability. We also develop game-theoretic foundations for the semantics of PCTL over Markov chains by capturing the standard PCTL semantics via a two-player games. These games, finally, inspire a notion of p-automata, which accept entire Markov chains. We show that p-automata subsume PCTL and Markov chains; that their languages of Markov chains have pleasant closure properties; and that the complexity of deciding acceptance matches that of probabilistic model checking for p-automata representing PCTL formulae. In addition, we offer a simulation between p-automata that under-approximates language containment. These results then allow us to show that p-automata comprise a solution to the problem studied in this thesis

Model-Checking Over Multi-Valued Logics

Classical logic cannot be used to effectively reason about systems with uncertainty (lack of essential information) or inconsistency (contradictory information often occurring when information is gathered from multiple sources). In this paper we propose the use of quasi-boolean multi-valued logics for reasoning about such systems. We also give semantics to a multi-valued extension of CTL, describe an implementation of a symbolic multi-valued CTL model-checker called chek, and analyze its correctness and running time

Data Structures for Symbolic Multi-Valued Model-Checking

Multi-valued logics can be effectively used to reason about incomplete and/or inconsistent systems, e.g. during early software requirements or as the systems evolve. In our earlier work we identified a useful family of multi-valued logics: those specified over finite distributive lattices where negation preserves involution, i.e., �¦������ � for every element � of the logic. Model-checking over this family of logics allows not only to extend the domain of applicability of automated reasoning to new problems, but also to speed up some classical verification problems. Symbolic model-checking over multi-valued logics can be cast in terms of operations over multivalued sets: sets whose membership functions are multi-valued. In this paper we propose and empirically evaluate several choices for implementing multi-valued sets with decision diagrams. In particular, we describe two major approaches: (1) representing the multi-valued membership function canonically, using MDDs or ADDs; (2) representing multi-valued sets as a collection of classical sets, using a vector of either MBTDDs or BDDs. The naive implementation of (2) includes having a classical set for each value of the logic. We exploit a result of lattice theory to reduce the number of such sets that need to be represented. The major contribution of this paper is the evaluation of the different implementations of multivalued sets, done via a series of experiments and using several case studies.