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424,510
Control of Markov Decision Processes from PCTL specifications
, 2011
"... Abstract — We address the problem of controlling a Markov Decision Process (MDP) such that the probability of satisfying a temporal logic specification over a set of properties associated to its states is maximized. We focus on specifications given as formulas of Probabilistic Computation Tree Logic ..."
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Abstract — We address the problem of controlling a Markov Decision Process (MDP) such that the probability of satisfying a temporal logic specification over a set of properties associated to its states is maximized. We focus on specifications given as formulas of Probabilistic Computation Tree
On the connections between PCTL and dynamic programming
 Proceedings of the 13th ACM international conference on Hybrid Systems: Computation and Control
"... ABSTRACT. Probabilistic Computation Tree Logic (PCTL) is a wellknown modal logic which has become a standard for expressing temporal properties of finitestate Markov chains in the context of automated model checking. In this paper, we give a definition of PCTL for noncountablespace Markov chains, ..."
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Cited by 9 (0 self)
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ABSTRACT. Probabilistic Computation Tree Logic (PCTL) is a wellknown modal logic which has become a standard for expressing temporal properties of finitestate Markov chains in the context of automated model checking. In this paper, we give a definition of PCTL for noncountablespace Markov chains
PolynomialTime Verification of PCTL Properties of MDPs with Convex Uncertainties
"... Abstract. We address the problem of verifying Probabilistic Computation Tree Logic (PCTL) properties of Markov Decision Processes (MDPs) whose state transition probabilities are only known to lie within uncertainty sets. We first introduce the model of ConvexMDPs (CMDPs), i.e., MDPs with convex unc ..."
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Cited by 4 (1 self)
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Abstract. We address the problem of verifying Probabilistic Computation Tree Logic (PCTL) properties of Markov Decision Processes (MDPs) whose state transition probabilities are only known to lie within uncertainty sets. We first introduce the model of ConvexMDPs (CMDPs), i.e., MDPs with convex
Don’t Know in probabilistic systems
 SPIN 2006. LNCS
, 2006
"... In this paper the abstractionrefinement paradigm based on 3valued logics is extended to the setting of probabilistic systems. We define a notion of abstraction for Markov chains. To be able to relate the behavior of abstract and concrete systems, we equip the notion of abstraction with the concep ..."
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Cited by 35 (2 self)
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with the concept of simulation. Furthermore, we present model checking for abstract probabilistic systems (abstract Markov chains) with respect to specifications in probabilistic temporal logics, interpreted over a 3valued domain. More specifically, we introduce a 3valued version of probabilistic computationtree
Symbolic model checking for probabilistic processes
 IN PROCEEDINGS OF ICALP '97
, 1997
"... We introduce a symbolic model checking procedure for Probabilistic Computation Tree Logic PCTL over labelled Markov chains as models. Model checking for probabilistic logics typically involves solving linear equation systems in order to ascertain the probability of a given formula holding in a stat ..."
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Cited by 97 (29 self)
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We introduce a symbolic model checking procedure for Probabilistic Computation Tree Logic PCTL over labelled Markov chains as models. Model checking for probabilistic logics typically involves solving linear equation systems in order to ascertain the probability of a given formula holding in a
On Compositionality, Efficiency, and Applicability of Abstraction in Probabilistic Systems
"... Abstract. A branching bisimulation for probabilistic systems that is preserved under parallel composition has been defined recently for the alternating model. We show that besides being compositional, it is decidable in polynomial time and it preserves the properties expressible in probabilistic Com ..."
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Computation Tree Logic (pCTL). In the groundcomplete axiomatization, only a single axiom is added to the axioms for strong bisimulation. We show that the Concurrent Alternating Bit protocol can be verified using the process algebra and a set of recursive rules. 1
Motion planning and control from temporal logic specifications with probabilistic satisfaction guarantees
 in ICRA, 2010
"... Abstract — We present a computational framework for automatic deployment of a robot from a temporal logic specification over a set of properties of interest satisfied at the regions of a partitioned environment. We assume that, during the motion of the robot in the environment, the current region c ..."
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Cited by 32 (5 self)
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. We propose an algorithm inspired from probabilistic Computation Tree Logic (PCTL) model checking to find a control strategy that maximizes the probability of satisfying the specification. We illustrate our method with simulation and experimental results. I.
Timeabstracting bisimulation for probabilistic timed automata
 In Proc. TASE’08
, 2008
"... This paper focuses on probabilistic timed automata (PTA), an extension of timed automata with discrete probabilistic branchings. As the regions of these automata often lead to an exponential blowup, reduction techniques are of utmost importance. In this paper, we investigate probabilistic timeab ..."
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Cited by 4 (0 self)
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abstracting bisimulation (PTAB), an equivalence notion that abstracts from exact time delays. PTAB is proven to preserve probabilistic computational tree logic (PCTL). The region equivalence is a (very refined) PTAB. Furthermore, we provide a nontrivial adaptation of the traditional partitionrefinement algorithm
Computing Science Group ABSTRACTION FRAMEWORK FOR MARKOV DECISION PROCESSES AND PCTL VIA GAMES
"... Markov decision processes (MDPs) are natural models of computation in a wide range of applications. Probabilistic computation tree logic (PCTL) is a powerful temporal logic for reasoning about and verifying such models. Often, these models are prohibitively large or infinitestate, and so direct mod ..."
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Markov decision processes (MDPs) are natural models of computation in a wide range of applications. Probabilistic computation tree logic (PCTL) is a powerful temporal logic for reasoning about and verifying such models. Often, these models are prohibitively large or infinitestate, and so direct
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
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Cited by 619 (31 self)
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of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related
Results 1  10
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424,510