A multi-agent approach to hyper-redundant manipulators

The ability to reach into con??ded and unorganized envi- ronments, and grasp and manipulate various objects make hyper-redundant robotic manipulators the ideal tool for a variety of applications. Such applications include search and rescue, exploration of unknown environments, assem- bly and manufacturing as well as robotic-surgery. A key challenge that has limited the applicability of such robotic manipulators is the di??culty in controlling a robot with a very large number of interacting components. This paper aims to address this issue by using a new adaptive multia- gent control approach for exible shape changes of so called snake-arms and discusses extensions including a universal, more versatile, tree-like robotic manipulator. Our prelim- inary results show the feasibility of the approach and the potential bene??ts of a multiagent approach which includes scalability, fault tolerance, adaptivity and automated load balancing. A key ??nding of this study is the necessity of principled credit assignment for the agents, so that their collective actions optimize the performance of the full robot structure

Formalizing multi-state learning dynamics

This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynamics, a combination of replicators and piecewise models to account for multi-state problems. We formalize this promising proof of concept and provide definitions for the notion of average reward games, pure equilibrium cells and finally, piecewise replicator dynamics. These definitions are general in the number of agents and states. Results show that piecewise replicator dynamics qualitatively approximate multi-agent reinforcement learning in stochastic games

State-coupled replicator dynamics

This paper introduces a new model, i.e. state-coupled replicator dynamics, expanding the link between evolutionary game theory and multiagent reinforcement learning to multistate games. More precisely, it extends and improves previous work on piecewise replicator dynamics, a combination of replicators and piecewise models. The contributions of the paper are twofold. One, we identify and explain the major shortcomings of piecewise replicators, i.e. discontinuities and occurrences of qualitative anomalies. Two, this analysis leads to the proposal of the new model for learning dynamics in stochastic games, named state-coupled replicator dynamics. The preceding formalization of piecewise replicators - general in the number of agents and states - is factored into the new approach. Finally, we deliver a comparative study of finite action-set learning automata to piecewise and state-coupled replicator dynamics. Results show that statecoupled replicators model learning dynamics in stochastic games more accurately than their predecessor, the piecewise approach

State-coupled replicator dynamics

This paper introduces a new model, i.e. state-coupled replicator dynamics, expanding the link between evolutionary game theory and multiagent reinforcement learning to multistate games. More precisely, it extends and improves previous work on piecewise replicator dynamics, a combination of replicators and piecewise models. The contributions of the paper are twofold. One, we identify and explain the major shortcomings of piecewise replicators, i.e. discontinuities and occurrences of qualitative anomalies. Two, this analysis leads to the proposal of the new model for learning dynamics in stochastic games, named state-coupled replicator dynamics. The preceding formalization of piecewise replicators - general in the number of agents and states - is factored into the new approach. Finally, we deliver a comparative study of finite action-set learning automata to piecewise and state-coupled replicator dynamics. Results show that statecoupled replicators model learning dynamics in stochastic games more accurately than their predecessor, the piecewise approach

A multi-agent approach to hyper-redundant manipulators

The ability to reach into con??ded and unorganized envi- ronments, and grasp and manipulate various objects make hyper-redundant robotic manipulators the ideal tool for a variety of applications. Such applications include search and rescue, exploration of unknown environments, assem- bly and manufacturing as well as robotic-surgery. A key challenge that has limited the applicability of such robotic manipulators is the di??culty in controlling a robot with a very large number of interacting components. This paper aims to address this issue by using a new adaptive multia- gent control approach for exible shape changes of so called snake-arms and discusses extensions including a universal, more versatile, tree-like robotic manipulator. Our prelim- inary results show the feasibility of the approach and the potential bene??ts of a multiagent approach which includes scalability, fault tolerance, adaptivity and automated load balancing. A key ??nding of this study is the necessity of principled credit assignment for the agents, so that their collective actions optimize the performance of the full robot structure

Micro-scale social network analysis for ultra-long space flights

This paper proposes to leverage the mathematical means of game theory to analyze on-board social crew dynamics. We describe how game theory facilitates capturing the essence of interactive decision making, thereby raising the potential for a fully automated and unintrusive monitoring and diagnosis tool. Finally, we present preliminary findings based on the base-line data collection and the first phase of the ground based Mars-500 isolation experiment

Micro-scale social network analysis for ultra-long space flights

This paper proposes to leverage the mathematical means of game theory to analyze on-board social crew dynamics. We describe how game theory facilitates capturing the essence of interactive decision making, thereby raising the potential for a fully automated and unintrusive monitoring and diagnosis tool. Finally, we present preliminary findings based on the base-line data collection and the first phase of the ground based Mars-500 isolation experiment