6533b82efe1ef96bd1293175
RESEARCH PRODUCT
Learning of Cooperative Behaviour in Robot Populations
Paul TroddenSandor M. VeresDario BausoMichalis Smyrnakissubject
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 theorydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-06-01 |