scholarly journals Conditional simple temporal networks with uncertainty and decisions

2019 ◽  
Vol 797 ◽  
pp. 77-101 ◽  
Author(s):  
Matteo Zavatteri ◽  
Luca Viganò
Keyword(s):  
Temporal Networks ◽  
Simple Temporal Networks

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.

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 Dynamic Control of Probabilistic Simple Temporal Networks

2020 ◽  
Vol 34 (06) ◽  
pp. 9851-9858
Author(s):  
Michael Gao ◽  
Lindsay Popowski ◽  
Jim Boerkoel
Keyword(s):  
Dynamic Scheduling ◽  
Dynamic Control ◽  
Probability Density Functions ◽  
Directed Search ◽  
Temporal Constraints ◽  
Temporal Networks ◽  
Loss Of Control ◽  
Dc Algorithm ◽  
Simple Temporal Networks ◽  
Real World Problems

The controllability of a temporal network is defined as an agent's ability to navigate around the uncertainty in its schedule and is well-studied for certain networks of temporal constraints. However, many interesting real-world problems can be better represented as Probabilistic Simple Temporal Networks (PSTNs) in which the uncertain durations are represented using potentially-unbounded probability density functions. This can make it inherently impossible to control for all eventualities. In this paper, we propose two new dynamic controllability algorithms that attempt to maximize the likelihood of successfully executing a schedule within a PSTN. The first approach, which we call Min-Loss DC, finds a dynamic scheduling strategy that minimizes loss of control by using a conflict-directed search to decide where to sacrifice the control in a way that optimizes overall success. The second approach, which we call Max-Gain DC, works in the other direction: it finds a dynamically controllable schedule and then attempts to progressively strengthen it by capturing additional uncertainty. Our approaches are the first known that work by finding maximally dynamically controllable schedules. We empirically compare our approaches against two existing PSTN offline dispatch approaches and one online approach and show that our Min-Loss DC algorithm outperforms the others in terms of maximizing execution success while maintaining competitive runtimes.

Sign in / Sign up

Export Citation Format

Share Document