Multiagent Robotic Systems

Multiagent Robotic Systems addresses learning and adaptation in decentralized autonomous robots. It provides a guided tour of the pioneering work and major technical issues in multiagent robotics research. Its systematic examination demonstrates the interrelationships between the autonomy of individual robots and the emerged global behavior properties of a group performing a cooperative task. The authors also include descriptions of the essential building blocks of the architecture of autonomous mobile robots with respect to their requirement on local behavioral conditioning and group behavioral evolution.

Providing a guided tour of the pioneering work and major technical issues, Multiagent Robotic Systems addresses learning and adaptation in decentralized autonomous robots. Its systematic examination demonstrates the interrelationships between the autonomy of individual robots and the emerged global behavior properties of a group performing a cooperative task. The author also includes descriptions of the essential building blocks of the architecture of autonomous mobile robots with respect to their requirement on local behavioral conditioning and group behavioral evolution.After reading this book you will be able to fully appreciate the strengths and usefulness of various approaches in the development and application of multiagent robotic systems. It covers:Why and how to develop and experimentally test the computational mechanisms for learning and evolving sensory-motor control behaviors in autonomous robotsHow to design and develop evolutionary algorithm-based group behavioral learning mechanisms for the optimal emergence of group behaviorsHow to enable group robots to converge to a finite number of desirable task states through group learningWhat are the effects of the local learning mechanisms on the emergent global behaviorsHow to use decentralized, self-organizing autonomous robots to perform cooperative tasks in an unknown environmentEarlier works have focused primarily on how to navigate in a spatially unknown environment, given certain predefined motion behaviors. What is missing, however, is an in-depth look at the important issues on how to effectively obtain such behaviors in group robots and how to enable behavioral learning and adaptation at the group level. Multiagent Robotic Systems examines the key methodological issues and gives you an understanding of the underlying computational models and techniques for multiagent systems.

MOTIVATION, APPROACHES, AND OUTSTANDING ISSUESWhy Multiple Robots?AdvantagesMajor ThemesAgents and Multiagent SystemsMultiagent RobotsTowards Cooperative ControlCooperation Related ResearchLearning, Evolution, and AdaptationDesign of Multi-Robot ControlApproachesBehavior-Based RoboticsCollective RoboticsEvolutionary RoboticsInspiration from Biology and SociologySummaryModels and TechniquesReinforcement LearningGenetic AlgorithmsArtificial LifeArtificial Immune SystemProbabilistic ModelingRelated Work on Multi-Robot Planning and CoordinationOutstanding Issues Self-OrganizationLocal vs. Global PerformancePlanningMulti-Robot learningCo-EvolutionEmergent BehaviorReactive vs. Symbolic SystemsHeterogeneous vs. Homogenous SystemsSimulated vs. Physical RobotsDynamics of Multiagent Robotic SystemsSummaryCASE STUDIES IN LEARNINGMultiagent Reinforcement Learning: TechniquesAutonomous Group RobotsMultiagent Reinforcement LearningSummaryMultiagent Reinforcement Learning ResultsMeasurementsGroup BehaviorsMultiagent Reinforcement Learning: What MattersCollective SensingInitial Spatial DistributionInverted Sigmoid FunctionBehavior Selection mechanismMotion MechanismEmerging a Periodic MotionMacro-Stable but Micro-Unstable PropertiesDominant BehaviorEvolutionary Multiagent Reinforcement LearningRobot Group ExampleEvolving Group Motion StrategiesExamplesSummaryCASE STUDIES IN ADAPTATIONCoordinated Maneuvers in a Dual-Agent SystemIssuesDual-Agent LearningSpecialized Roles in a Dual-Agent SystemThe Basic Capabilities of the Robot AgentThe Rationale of the Advice-Giving AgentAcquiring Complex ManeuversSummaryCollective BehaviorGroup BehaviorThe ApproachCollective Box-Pushing by Applying Repulsive ForcesCollective Box-Pushing by Exerting External Contact Forces and TorquesConvergence Analysis for the Fittest-Preserved EvolutionSummaryCASE STUDIES IN SELF-ORGANIZATIONMultiagent Self-OrganizationArtificial Potential FieldOverview of Self-OrganizationSelf-Organization of a Potential MapExperiment 1Experiment 2DiscussionsEvolutionary Multiagent Self-OrganizationEvolution of Cooperative Motion StrategiesExperimentsDiscussionsSummaryAN EXPLORATION TOOLToolboxes for Multiagent RoboticsOverviewToolbox for Multiagent Reinforcement LearningToolbox for Evolutionary Multiagent Reinforcement LearningToolboxes for Evolutionary Collective Behavior ImplementationToolbox for Multiagent Self-OrganizationToolbox for Evolutionary Multiagent Self-OrganizationExampleINDEX