Quantitative temporal reasoning

Emerson, E. Allen; Mok, Aloysius K.; Sistla, A. Prasad; Srinivasan, Jai

doi:10.1007/bfb0023727

Cited by 99 publications

(40 citation statements)

References 26 publications

(8 reference statements)

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“…Verus allows the verification of untimed properties expressed as CTL formulas (Clarke, 1986 andMcMillan 1992) as well as oftimed properties expressed as RTCTL formulas (Emerson, 1990). CTL formulas allow the verification of properties such "p will eventually occur", or "p will never be asserted".…”

Section: Ctl and Rtctl Model Checkingmentioning

confidence: 99%

“…The model uses a discrete model of time, in which each transition corresponds to one time unit. A symbolic model checker allows the verification of un timed properties expressed in CTL (Clarke, 1986 andMcMillan, 1992) and of time bounded properties using RTCTL model checking (Emerson, 1990). Moreover, algorithms derived from symbolic model checking are used to compute quantitative infonnation about the model (Campos, 1996a;1995a and1995c).…”

Section: Introductionmentioning

confidence: 99%

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Shared Variables and Efficient Synchronization Primitives for Synchronous Symbolic Verifiers

Campos¹

1998

IFIP Advances in Information and Communication Technology

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In spite of the increasingly frequent application of formal verification tools today they still are difficult to use for non trivial verification tasks. One reason is that the specification languages used by these systems are still quite different than the programming languages designers use. One significant difference is the absence of shared variables for synchronous verifiers. Many systems use shared variables in their implementation, but most synchronous formal verification tools do not model them. Instead the user must use additional variables to maintain the illusion of shared memory and model the correct behavior. Unfortunately this can cause a significant overhead making the final model much larger than necessary. This work proposes a new technique called BDD markings to overcome this problem. BOD markings provide a way of keeping track of control flow information and have allowed us to implement shared variables in Verus, a synchronous symbolic verifier. Using the new method we have been able to verify a variant of a mutual exclusion algorithm used in the Mach operating system and to determine the existence of priority inversion in a complex real-time system. Priority inversion is a serious and subtle problem that can cause real-time systems to fail unexpectedly. Its importance has been highlighted recently when it was discovered that priority inversion has caused system shutdowns in the Pathfinder spacecraft soon after it started collecting Martian meteorological data. This example has been modeled with and without shared variables. The new method has allowed verification to be performed up to 10 times faster and using up to 20 times less memory, demonstrating the efficiency of the technique proposed.

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Section: Ctl and Rtctl Model Checkingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shared Variables and Efficient Synchronization Primitives for Synchronous Symbolic Verifiers

Campos¹

1998

IFIP Advances in Information and Communication Technology

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“…N.Y. [13,15] [10,13] {s} {e} [3,5] [ 7,9] NAVY I AirforceEnd AirforceStart Airforce Figure 1: The constraint graph of the logistics problem. X N:Y: = time point at which the cargo was shipped out of N.Y., X Chicago = time point the cargo arrived into and shipped out of Chicago X L:A: = time point at which the cargo arrived into L.A.…”

Section: Relationmentioning

confidence: 99%

Processing disjunctions in temporal constraint networks

Schwalb

Dechter

1997

Artificial Intelligence

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The framework of Temporal constraint Satisfaction Problems (TCSP) has been proposed for representing and processing temporal knowledge. Deciding consistency of TC-SPs is known to be intractable. As demonstrates in this paper, even local consistency algorithms like path-consistency can be exponential due to the fragmentation problem. We present t wo new polynomial approximation algorithms, Upper-Lower-Tightening (ULT) and Loose-Path-Consistency (LPC), which are ecient y et eective in detecting inconsistencies and reducing fragmentation. The experiments we performed on hard problems in the transition region show that LPC is the superior algorithm. When incorporated within backtrack search LPC is capable of improving performance by orders of magnitude. Problems involving temporal constraints arise in various areas such as temporal databases [6], diagnosis [11], scheduling [22, 2 1 ], planning [16], common-sense reasoning [25] and natural language understanding [1]. Several formalisms for expressing and reasoning about temporal constraints have been proposed; interval algebra [2], point algebra [29], Temporal Constraint Satisfaction Problems (TCSP) [7] and the model of combined quantitative and qualitative constraints [17, 12].The two t ypes of Temporal Constraint Networks that have emerged are qualitative [2] and quantitative [7]. In the qualitative model, variables are time intervals and the constraints are qualitative. In the quantitative model, variables represent time points and the constraints are metric. Subsequently, these two t ypes were combined into a single model [17,12]. In this paper we build upon the model proposed by [17], whose variables are either points or intervals and involves three types of constraints: metric point-point and qualitative pointinterval and interval-interval.Answering queries in constraint processing reduces to the tasks of determining consistency, computing a consistent scenario and computing the minimal network. When time is represented by rational numbers 1 , deciding consistency is in NP-complete [7,17]. For qualitative networks, computing the minimal network is in NP-hard [10,7]. In both qualitative and quantitative models, the source of complexity stems from allowing disjunctive relationships between pairs of variables. Such constraints often arise in many applications, as demonstrated by the following example:

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“…Several authors have tried to adapt temporal logic to reason about real-time properties by interpreting its modalities as bounded operators. For example, [Ko89] suggests the notation O<, to express "eventually within time c." Similar temporal operators that are subscripted with constant bounds are used in [Ha88] and [EMSS89].…”

Section: Tptl With Quantification Over Propositionsmentioning

confidence: 99%