A Tractable Generalization of Simple Temporal Networks and Its Relation to Mean Payoff Games

2014 ◽  
Author(s):  
Carlo Comin ◽  
Roberto Posenato ◽  
Romeo Rizzi
Keyword(s):  
Temporal Networks ◽  
Mean Payoff Games ◽  
Simple Temporal Networks ◽  
Mean Payoff

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.

USING STRATEGY IMPROVEMENT TO STAY ALIVE

2012 ◽  
Vol 23 (03) ◽  
pp. 585-608 ◽  
Author(s):  
LUBOŠ BRIM ◽  
JAKUB CHALOUPKA
Keyword(s):  
Experimental Study ◽  
Energy Level ◽  
Initial Energy ◽  
Minimum Amount ◽  
Usual Sense ◽  
Mean Payoff Games ◽  
Mean Payoff ◽  
Novel Algorithm

We design a novel algorithm for solving Mean-Payoff Games (MPGs). Besides solving an MPG in the usual sense, our algorithm computes more information about the game, information that is important with respect to applications. The weights of the edges of an MPG can be thought of as a gained/consumed energy – depending on the sign. For each vertex, our algorithm computes the minimum amount of initial energy that is sufficient for player Max to ensure that in a play starting from the vertex, the energy level never goes below zero. Our algorithm is not the first algorithm that computes the minimum sufficient initial energies, but according to our experimental study it is the fastest algorithm that computes them. The reason is that it utilizes the strategy improvement technique which is very efficient in practice.

scholarly journals Potential theory for mean payoff games

2007 ◽  
Vol 145 (3) ◽  
pp. 4967-4974 ◽  
Author(s):  
Y. M. Lifshits ◽  
D. S. Pavlov
Keyword(s):  
Potential Theory ◽  
Mean Payoff Games ◽  
Mean Payoff
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