scholarly journals Dynamic Consistency of Conditional Simple Temporal Networks via Mean Payoff Games: A Singly-Exponential Time DC-checking

2015 ◽  
Author(s):  
Carlo Comin ◽  
Romeo Rizzi
Keyword(s):  
Dynamic Consistency ◽  
Temporal Networks ◽  
Exponential Time ◽  
Mean Payoff Games ◽  
Simple Temporal Networks ◽  
Mean Payoff

scholarly journals Simpler and Faster Algorithm for Checking the Dynamic Consistency of Conditional Simple Temporal Networks

2018 ◽  
Author(s):  
Luke Hunsberger ◽  
Roberto Posenato
Keyword(s):  
Recent Work ◽  
Constraint Propagation ◽  
Dynamic Consistency ◽  
Temporal Networks ◽  
Alternative Semantics ◽  
Open Questions ◽  
Simpler Algorithm ◽  
Simple Temporal Networks ◽  
Propagation Rules

Recent work on Conditional Simple Temporal Networks (CSTNs) has focused on checking the dynamic consistency (DC) property assuming that execution strategies can react instantaneously to observations. Three alternative semantics---IR-DC, 0-DC, and π-DC---have been presented. The most practical DC-checking algorithm for CSTNs has only been analyzed with respect to the IR-DC semantics, while the 0-DC semantics was shown to have a serious flaw that the π-DC semantics fixed. Whether the IR-DC semantics had the same flaw and, if so, what the consequences would be for the DC-checking algorithm remained open questions. This paper (1) shows that the IR-DC semantics is also flawed; (2) shows that one of the constraint-propagation rules from the IR-DC-checking algorithm is not sound with respect to the IR-DC semantics; (3) presents a simpler algorithm, called the π-DC-checking algorithm; (4) proves that it is sound and complete with respect to the π-DC semantics; and (5) empirically evaluates the new algorithm.

scholarly journals Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions

2020 ◽  
Author(s):  
Michael Saint-Guillain ◽  
Tiago Stegun Vaquero ◽  
Jagriti Agrawal ◽  
Steve Chien
Keyword(s):  
Probability Distributions ◽  
Dynamic Control ◽  
Success Probability ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Temporal Networks ◽  
Fixed Parameter ◽  
Probability Of Success ◽  
Dynamic Policy ◽  
Simple Temporal Networks

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined probability distributions. This paper addresses on PSTN Dynamic Controllability (DC) robustness measure, i.e. the execution success probability of a network under dynamic control. We consider PSTNs where the probability distributions of the contingent edges are ordinary distributed (e.g. non-parametric, non-symmetric). We introduce the concepts of dispatching protocol (DP) as well as DP-robustness, the probability of success under a predefined dynamic policy. We propose a fixed-parameter pseudo-polynomial time algorithm to compute the exact DP-robustness of any PSTN under NextFirst protocol, and apply to various PSTN datasets, including the real case of planetary exploration in the context of the Mars 2020 rover, and propose an original structural analysis.

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