0000000000297213
AUTHOR
Michalis Smyrnakis
Game-Theoretic Learning and Allocations in Robust Dynamic Coalitional Games
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...
Learning of Cooperative Behaviour in Robot Populations
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.