CSTNU Tool: A Java library for checking temporal networks

Posenato, Roberto

doi:10.1016/j.softx.2021.100905

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…We implemented all algorithms in Java, available in version 4.12 of the CSTNU Tool of Posenato (2022), and ran them on a JVM 21 with 8 GB of heap memory on a Linux computer with two AMD Opteron ™ 4334@3.1 GHz (6200 BogoMIPS).…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…The results highlight the influence of the number of edges on the computing time, which is O(kn 3 ) in the worst case. When the input instances are generated by Morris14, the average (Hunsberger and Posenato 2024); an open-source Java implementation is available in CSTNU Tool v. 1.42 (Posenato 2022). Figure 10 demonstrates the impact of the number of edges on the performance of the ESTNU executor RTE * .…”

Section: Empirical Evaluationmentioning

confidence: 99%

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Converting Simple Temporal Networks with Uncertainty into Minimal Equivalent Dispatchable Form

Hunsberger,

Posenato

2024

ICAPS

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A Simple Temporal Network with Uncertainty (STNU) is a structure for representing and reasoning about time constraints on actions that may have uncertain durations. An STNU is dynamically controllable (DC) if there exists a dynamic strategy for executing the network that guarantees that all of its constraints will be satisfied no matter how the uncertain durations turn out---within their specified bounds. However, such strategies typically require exponential space. Therefore, converting a DC STNU into a so-called dispatchable form for practical applications is essential. The relevant portions of a real-time execution strategy for a dispatchable STNU can be incrementally constructed during execution, requiring only O(n²) space, while also providing maximum flexibility and minimal computation during the execution of the network. Although existing algorithms can generate equivalent-dispatchable STNUs, they do not guarantee a minimal number of edges in the STNU graph. Since the number of edges directly impacts the computations during execution, this paper presents a novel algorithm for converting any dispatchable STNU into an equivalent dispatchable network having a minimal number of edges. The complexity of the algorithm is O(k n³), where k is the number of actions with uncertain durations, and n is the number of timepoints in the network. The paper also provides an empirical evaluation of the reduction of edges obtained by the impact of the new algorithm.

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Section: Empirical Evaluationmentioning

confidence: 99%

Section: Empirical Evaluationmentioning

confidence: 99%

Converting Simple Temporal Networks with Uncertainty into Minimal Equivalent Dispatchable Form

Hunsberger,

Posenato

2024

ICAPS

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“…The performance of our new RUL2021 DC-checking algorithm was compared against that of the pre-existing heap memory on a Linux box with one Intel(R) Xeon(R) CPU E5-2637 v4 @ 3.50GHz. The implementations are freely available as a Java library (Posenato 2022).…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…We set up a generator that randomly generates realistic STNUs with a chosen topology, tunable by multiple parameters. The benchmarks are available from Posenato (2022). Because DC STNUs tend to be more challenging for DCchecking algorithms, this section focuses on DC STNUs.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Speeding Up the RUL¯ Dynamic-Controllability-Checking Algorithm for Simple Temporal Networks with Uncertainty

Hunsberger

Posenato

2022

AAAI

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A Simple Temporal Network with Uncertainty (STNU) includes real-valued variables, called time-points; binary difference constraints on those time-points; and contingent links that represent actions with uncertain durations. STNUs have been used for robot control, web-service composition, and business processes. The most important property of an STNU is called dynamic controllability (DC); and algorithms for checking this property are called DC-checking algorithms. The DC-checking algorithm for STNUs with the best worst-case time-complexity is the RUL¯ algorithm due to Cairo, Hunsberger and Rizzi. Its complexity is O(mn + k²n + kn log n), where n is the number of time-points, m is the number of constraints, and k is the number of contingent links. It is expected that this worst-case complexity cannot be improved upon. However, this paper provides a new algorithm, called RUL2021, that improves its performance in practice by an order of magnitude, as demonstrated by a thorough empirical evaluation.

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