0000000000297213

AUTHOR

Michalis Smyrnakis

showing 2 related works from this author

Game-Theoretic Learning and Allocations in Robust Dynamic Coalitional Games

2019

The problem of allocation in coalitional games with noisy observations and dynamic environments is considered. The evolution of the excess is modeled by a stochastic differential inclusion involvin...

Computer Science::Computer Science and Game Theory0209 industrial biotechnology020901 industrial engineering & automationControl and OptimizationDifferential inclusionGame theoreticApplied Mathematics010102 general mathematics02 engineering and technology0101 mathematics01 natural sciencesMathematical economicsMathematicsSIAM Journal on Control and Optimization
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Learning of Cooperative Behaviour in Robot Populations

2016

This paper addresses convergence and equilibrium properties of game theoretic learning algorithms in robot populations using simple and broadly applicable reward/cost models of cooperation between robotic agents. New models for robot cooperation are proposed by combining regret based learning methods and network evolution models. Results of mean-field game theory are employed in order to show the asymptotic second moment boundedness in the variation of cooperative behaviour. The behaviour of the proposed models are tested in simulation results, which are based on sample networks and a single lane traffic flow case study.

0209 industrial biotechnologyEngineeringbusiness.industryRegretSample (statistics)02 engineering and technologyVariation (game tree)Traffic flowRobot kinematics Automobiles Service robots Convergence Games020901 industrial engineering & automationSettore ING-INF/04 - AutomaticaSimple (abstract algebra)Convergence (routing)0202 electrical engineering electronic engineering information engineeringRobot020201 artificial intelligence & image processingArtificial intelligenceSettore MAT/09 - Ricerca OperativabusinessGame theory
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