The temporal logic of reactive and concurrent systems
The temporal logic of reactive and concurrent systems
The complexity of mean payoff games on graphs
Theoretical Computer Science
Competitive Markov decision processes
Competitive Markov decision processes
Model checking
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Time and Probability in Formal Design of Distributed Systems
Time and Probability in Formal Design of Distributed Systems
Introduction to Algorithms
Model Checking of Probabalistic and Nondeterministic Systems
Proceedings of the 15th Conference on Foundations of Software Technology and Theoretical Computer Science
Model Checking Continuous-Time Markov Chains by Transient Analysis
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Bisimulation for labelled Markov processes
Information and Computation - Special issue: LICS'97
Quantitative Analysis and Model Checking
LICS '97 Proceedings of the 12th Annual IEEE Symposium on Logic in Computer Science
Model checking for probability and time: from theory to practice
LICS '03 Proceedings of the 18th Annual IEEE Symposium on Logic in Computer Science
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Discounting the future in systems theory
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
A temporal logic for Markov chains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Model Checking Quantitative Linear Time Logic
Electronic Notes in Theoretical Computer Science (ENTCS)
A Temporal Logic for Stochastic Multi-Agent Systems
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
A formal language toward the unification of model checking and performance evaluation
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
From boolean to quantitative synthesis
EMSOFT '11 Proceedings of the ninth ACM international conference on Embedded software
Specification and verification of multi-agent systems
ESSLLI'10 Proceedings of the 2010 conference on ESSLLI 2010, and ESSLLI 2011 conference on Lectures on Logic and Computation
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
Automatic vandalism detection in wikipedia with active associative classification
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
Formalizing and reasoning about quality
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
More or less true: DCTL for continuous-time MDPs
FORMATS'13 Proceedings of the 11th international conference on Formal Modeling and Analysis of Timed Systems
Simulation for lattice-valued doubly labeled transition systems
International Journal of Approximate Reasoning
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Temporal logic is two-valued: formulas are interpreted as either true or false. When applied to the analysis of stochastic systems, or systems with imprecise formal models, temporal logic is therefore fragile: even small changes in the model can lead to opposite truth values for a specification. We present a generalization of the branching-time logic CTL which achieves robustness with respect to model perturbations by giving a quantitative interpretation to predicates and logical operators, and by discounting the importance of events according to how late they occur. In every state, the value of a formula is a real number in the interval [0,1], where 1 corresponds to truth and 0 to falsehood. The boolean operators and and or are replaced by min and max, the path quantifiers ∃ and ¬ determine sup and inf over all paths from a given state, and the temporal operators ♦ and □ specify sup and inf over a given path; a new operator averages all values along a path. Furthermore, all path operators are discounted by a parameter that can be chosen to give more weight to states that are closer to the beginning of the path.We interpret the resulting logic DCTL over transition systems, Markov chains, and Markov decision processes. We present two semantics for DCTL: a path semantics, inspired by the standard interpretation of state and path formulas in CTL, and a fixpoint semantics, inspired by the µ-calculus evaluation of CTL formulas. We show that, while these semantics coincide for CTL, they differ for DCTL, and we provide model-checking algorithms for both semantics.