scholarly journals Mean-payoff games with partial observation

2018 ◽  
Vol 735 ◽  
pp. 82-110 ◽  
Author(s):  
Paul Hunter ◽  
Arno Pauly ◽  
Guillermo A. Pérez ◽  
Jean-François Raskin
Keyword(s):  
Partial Observation ◽  
Mean Payoff Games ◽  
Mean Payoff

scholarly journals Mean-Payoff Games with Partial-Observation

2014 ◽  
pp. 163-175 ◽  
Author(s):  
Paul Hunter ◽  
Guillermo A. Pérez ◽  
Jean-François Raskin
Keyword(s):  
Partial Observation ◽  
Mean Payoff Games ◽  
Mean Payoff

scholarly journals Local Mean Payoff Supervisory Control under Partial Observation

2020 ◽  
Vol 53 (4) ◽  
pp. 390-396
Author(s):  
Yiding Ji ◽  
Xiang Yin ◽  
Wei Xiao
Keyword(s):  
Supervisory Control ◽  
Partial Observation ◽  
Local Mean ◽  
Mean Payoff

Optimal supervisory control with mean payoff objectives and under partial observation

Automatica ◽  
2021 ◽  
Vol 123 ◽  
pp. 109359
Author(s):  
Yiding Ji ◽  
Xiang Yin ◽  
Stéphane Lafortune
Keyword(s):  
Supervisory Control ◽  
Partial Observation ◽  
Mean Payoff

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

The complexity of mean payoff games

1995 ◽  
pp. 1-10 ◽  
Author(s):  
Uri Zwick ◽  
Michael S. Paterson
Keyword(s):  
Mean Payoff Games ◽  
Mean Payoff

scholarly journals Combinatorial Simplex Algorithms Can Solve Mean Payoff Games

2014 ◽  
Vol 24 (4) ◽  
pp. 2096-2117 ◽  
Author(s):  
Xavier Allamigeon ◽  
Pascal Benchimol ◽  
Stéphane Gaubert ◽  
Michael Joswig
Keyword(s):  
Mean Payoff Games ◽  
Mean Payoff

Positional strategies for mean payoff games

1979 ◽  
Vol 8 (2) ◽  
pp. 109-113 ◽  
Author(s):  
A. Ehrenfeucht ◽  
J. Mycielski
Keyword(s):  
Mean Payoff Games ◽  
Mean Payoff
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