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11
Probabilistic Simulations for Probabilistic Processes
, 1994
"... Several probabilistic simulation relations for probabilistic systems are defined and evaluated according to two criteria: compositionality and preservation of "interesting" properties. Here, the interesting properties of a system are identified with those that are expressible in an untimed version o ..."
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Cited by 266 (18 self)
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Several probabilistic simulation relations for probabilistic systems are defined and evaluated according to two criteria: compositionality and preservation of "interesting" properties. Here, the interesting properties of a system are identified with those that are expressible in an untimed version of the Timed Probabilistic concurrent Computation Tree Logic (TPCTL) of Hansson. The definitions are made, and the evaluations carried out, in terms of a general labeled transition system model for concurrent probabilistic computation. The results cover weak simulations, which abstract from internal computation, as well as strong simulations, which do not.
Reasoning about Knowledge and Probability
 Journal of the ACM
, 1994
"... : We provide a model for reasoning about knowledge and probability together. We allow explicit mention of probabilities in formulas, so that our language has formulas that essentially say "according to agent i, formula ' holds with probability at least b." The language is powerful enough to allow r ..."
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Cited by 154 (15 self)
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: We provide a model for reasoning about knowledge and probability together. We allow explicit mention of probabilities in formulas, so that our language has formulas that essentially say "according to agent i, formula ' holds with probability at least b." The language is powerful enough to allow reasoning about higherorder probabilities, as well as allowing explicit comparisons of the probabilities an agent places on distinct events. We present a general framework for interpreting such formulas, and consider various properties that might hold of the interrelationship between agents' probability assignments at different states. We provide a complete axiomatization for reasoning about knowledge and probability, prove a small model property, and obtain decision procedures. We then consider the effects of adding common knowledge and a probabilistic variant of common knowledge to the language. A preliminary version of this paper appeared in the Proceedings of the Second Conference on T...
Module Checking
, 1996
"... . In computer system design, we distinguish between closed and open systems. A closed system is a system whose behavior is completely determined by the state of the system. An open system is a system that interacts with its environment and whose behavior depends on this interaction. The ability of ..."
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Cited by 79 (11 self)
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. In computer system design, we distinguish between closed and open systems. A closed system is a system whose behavior is completely determined by the state of the system. An open system is a system that interacts with its environment and whose behavior depends on this interaction. The ability of temporal logics to describe an ongoing interaction of a reactive program with its environment makes them particularly appropriate for the specification of open systems. Nevertheless, modelchecking algorithms used for the verification of closed systems are not appropriate for the verification of open systems. Correct model checking of open systems should check the system with respect to arbitrary environments and should take into account uncertainty regarding the environment. This is not the case with current modelchecking algorithms and tools. In this paper we introduce and examine the problem of model checking of open systems (mod ule checking, for short). We show that while module che...
Knowledge, probability, and adversaries
 Journal of the ACM
, 1993
"... Abstract: What should it mean for an agent toknowor believe an assertion is true with probability:99? Di erent papers [FH88, FZ88a, HMT88] givedi erent answers, choosing to use quite di erent probability spaces when computing the probability that an agent assigns to an event. We showthat each choice ..."
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Cited by 71 (23 self)
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Abstract: What should it mean for an agent toknowor believe an assertion is true with probability:99? Di erent papers [FH88, FZ88a, HMT88] givedi erent answers, choosing to use quite di erent probability spaces when computing the probability that an agent assigns to an event. We showthat each choice can be understood in terms of a betting game. This betting game itself can be understood in terms of three types of adversaries in uencing three di erent aspects of the game. The rst selects the outcome of all nondeterministic choices in the system� the second represents the knowledge of the agent's opponent in the betting game (this is the key place the papers mentioned above di er) � the third is needed in asynchronous systems to choose the time the bet is placed. We illustrate the need for considering all three types of adversaries with a number of examples. Given a class of adversaries, we show howto assign probability spaces to agents in a way most appropriate for that class, where \most appropriate " is made precise in terms of this betting game. We conclude by showing how di erent assignments of probability spaces (corresponding to di erent opponents) yield di erent levels of guarantees in probabilistic coordinated attack.
Module checking revisited
 In Proc. 9th CAV, LNCS 1254
, 1997
"... Abstract. When we verify the correctness of an open system with respect to a desired requirement, we should take into consideration the different environments with which the system may interact. Each environment induces a different behavior of the system, and we want all these behaviors to satisfy t ..."
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Cited by 30 (6 self)
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Abstract. When we verify the correctness of an open system with respect to a desired requirement, we should take into consideration the different environments with which the system may interact. Each environment induces a different behavior of the system, and we want all these behaviors to satisfy the requirement. Module checking is an algorithmic method that checks, given an open system (modeled as a finite structure) and a desired requirement (specified by a temporallogic formula), whether the open system satisfies the requirement with respect to all environments. In this paper we extend the modulechecking method with respect to two orthogonal issues. Both issues concern the fact that often we are not interested in satisfaction of the requirement with respect to all environments, but only with respect to these that meet some restriction. We consider the case where the environment has incomplete information about the system; i.e., when the system has internal variables, which are not readable by its environment, and the case where some assumptions are known about environment; i.e., when the system is guaranteed to satisfy the requirement only when its environment satisfies certain assumptions. We study the complexities of the extended modulechecking problems. In particular, we show that for universal temporal logics (e.g., LTL, ¥ CTL, and ¥ CTL ¦), module checking with incomplete information coincides with module checking, which by itself coincides with model checking. On the other hand, for nonuniversal temporal logics (e.g., CTL and CTL ¦), module checking with incomplete information is harder than module checking, which is by itself harder than model checking. 1
The wakeup problem
 SIAM Journal on Computing
, 1996
"... We study a new problem, the wakeup problem, that seems to be fundamental in distributed computing. We present efficient solutions to the problem and show how these solutions can be used to solve the consensus problem, the leader election problem, and other related problems. The main question we try ..."
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Cited by 24 (5 self)
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We study a new problem, the wakeup problem, that seems to be fundamental in distributed computing. We present efficient solutions to the problem and show how these solutions can be used to solve the consensus problem, the leader election problem, and other related problems. The main question we try to answer is, how much memory is needed to solve the wakeup problem? We assume a model that captures important properties of real systems that have been largely ignored by previous work on cooperative problems.
A logic for reasoning about evidence
 In Proc. 19th Conference on Uncertainty in Artificial Intelligence (UAI’03
, 2003
"... We introduce a logic for reasoning about evidence that essentially views evidence as a function from prior beliefs (before making an observation) to posterior beliefs (after making the observation). We provide a sound and complete axiomatization for the logic, and consider the complexity of the deci ..."
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Cited by 12 (1 self)
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We introduce a logic for reasoning about evidence that essentially views evidence as a function from prior beliefs (before making an observation) to posterior beliefs (after making the observation). We provide a sound and complete axiomatization for the logic, and consider the complexity of the decision problem. Although the reasoning in the logic is mainly propositional, we allow variables representing numbers and quantification over them. This expressive power seems necessary to capture important properties of evidence. 1.
Robust Satisfaction
, 1999
"... In order to check whether an open system satisfies a desired property, we need to check the behavior of the system with respect to an arbitrary environment. In the most general setting, the environment is another open system. Given an open system � and a property � , we say that � robustly satisfie ..."
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Cited by 6 (3 self)
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In order to check whether an open system satisfies a desired property, we need to check the behavior of the system with respect to an arbitrary environment. In the most general setting, the environment is another open system. Given an open system � and a property � , we say that � robustly satisfies � iff for every open system �� � , which serves as an environment to � , the composition ���� � � satisfies �. The problem of robust model checking is then to decide, given � and � , whether � robustly satisfies �. In this paper we study the robustmodelchecking problem. We consider systems modeled by nondeterministic Moore machines, and properties specified by branching temporal logic (for linear temporal logic, robust satisfaction coincides with usual satisfaction). We show that the complexity of the problem is EXPTIMEcomplete for CTL and the �calculus, and is 2EXPTIMEcomplete for CTL �. We partition branching temporal logic formulas into three classes: universal, existential, and mixed formulas. We show that each class has different sensitivity to the robustness requirement. In particular, unless the formula is mixed, robust model checking can ignore nondeterministic environments. In addition, we show that the problem of classifying a CTL formula into these classes is EXPTIMEcomplete.
Knowledge in Shared Memory Systems
 Proceedings of the Tenth ACM Symposium on Principles of Distributed Computing
, 1991
"... We study the relation between knowledge and space. That is, we analyze how much shared memory space is needed in order to learn certain kinds of facts. Such results are useful tools for reasoning about shared memory systems. In addition we generalize a known impossibility result, and show that resul ..."
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Cited by 2 (0 self)
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We study the relation between knowledge and space. That is, we analyze how much shared memory space is needed in order to learn certain kinds of facts. Such results are useful tools for reasoning about shared memory systems. In addition we generalize a known impossibility result, and show that results about how knowledge can be gained and lost in message passing systems also hold for shared memory systems.
A Process Algebraic Approach To FaultTolerance
 In Proceedings of the 15th Australian Computer Science Conference
, 1992
"... A process algebraic approach to the speci#cation of fault tolerant systems is described. As replication is inevitable for fault tolerance, we extend the process algebra of Aceto and Hennessy with a replication operator. An operational semantics for replicated processes with majority voting is dev ..."
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Cited by 1 (1 self)
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A process algebraic approach to the speci#cation of fault tolerant systems is described. As replication is inevitable for fault tolerance, we extend the process algebra of Aceto and Hennessy with a replication operator. An operational semantics for replicated processes with majority voting is developed. We model faults as action re#nement and showhow the e#ect of faults on a replicated system can be modelled. 1 Introduction In this paper we present a process algebraic approach to the semantics of fault tolerant systems. A fault is an event which causes the system to deviate from its expected behaviour. Faults may be due to software errors #bugs#, hardware design errors, physical malfunctions due to factors such as fatigue. A system speci#cation usually makes certain assumptions about the environment in which it operates. If the environment behaves in an unexpected fashion, the behaviour of the system cannot be predicted. Suchchanges in the operating environmentmay also be called ...