Learning to role-switch in multi-robot systems

Martinson, Eric; Arkin, Ronald C.

doi:10.1109/robot.2003.1242005

Cited by 27 publications

(17 citation statements)

References 16 publications

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“…Machine learning algorithms relieve human programmers of this duty by having the agents learn their own policies. For example, Martinson and Arkin [103] use reinforcement learning (RL) to learn the role switching policies for a team of robots cooperating on a military mission. In highly dynamic domains, agents need to react quickly to environmental changes and cannot always rely on communication when making role assignment decisions [151].…”

Section: Non-communicative Methodsmentioning

confidence: 99%

“…Martinson and Arkin [103] Reinforcement learning used to learn role-switching policies computation costs of combinatorial auctions while still allowing agents to take in to account interdependencies between tasks or roles when making their bids. Negotiation-based coordination strategies are well-suited for the role allocation problem because the large amount of existing work on problems such as task allocation can be modified for role allocation.…”

Section: No Communicationmentioning

confidence: 99%

“…Note the difference between Balch's method and Martinson and Arkin's [103] method discussed in Sect. 5.1.5.…”

Section: Role-based Behaviors and Machine Learning Techniquesmentioning

confidence: 99%

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Multi-agent role allocation: issues, approaches, and multiple perspectives

Campbell

2010

Auton Agent Multi-Agent Syst

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In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multi-agent systems. Along with a discussion of these issues about roles in multi-agent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multi-agent systems.

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Section: Non-communicative Methodsmentioning

confidence: 99%

Section: No Communicationmentioning

confidence: 99%

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Multi-agent role allocation: issues, approaches, and multiple perspectives

Campbell

2010

Auton Agent Multi-Agent Syst

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“…For instance, a trainer can switch his/her role with a trainee so that he/she can show a specific manipulation or gesture as he/she would have done in real life. In the Intelligent Robots field, it has been studied for several years [16,20] . It is used to improve the achievement of collaborative tasks by giving the possibility of switching the role assigned to robots between leader and follower to speed up a task and to resolve conflicts.…”

Section: Exchange Of Avatars In Cvetmentioning

confidence: 99%

An interaction abstraction model for seamless avatar exchange in CVET

Bouville

Lopez

Nouviale

et al. 2014

2014 International Workshop on Collaborative Virtual Environments (3DCVE)

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Collaboration and interaction between users and virtual humans in virtual environments is a crucial challenge, notably for Collaborative Virtual Environments for Training (CVET). A training procedure, indeed, often involves several actors: trainees, teammates and many times a trainer. Yet, a major benefit of CVET is to propose to users to be trained even if the required number of person needed by the procedure is not available. Therefore, almost every CVET use autonomous virtual humans to replace the missing person. In this paper, we present the main results of our project that aims at improving the effective collaboration between users and virtual humans involved in a complex task within CVET. Using an entity called the "Shell", we are able to wrap the features common to both users and virtual humans. It gives us an abstraction level to pool the management of the main processes useful to control an avatar, interact with the environment and gather knowledge from a CVET. Besides, the Shell allows seamless exchange of avatars during a procedure. Thanks to the Shell, the exchange can be carried out at any time during a task while preserving all the data associated to a role in a procedure.

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“…Platforms such as iRobot and Swarm-Bot are two well known examples of swarm robotics. In general, the idea of swarm robotics is widely used in military-based applications such as counter terrorism and bomb or threat detectors, moving products in large warehouses, and for search and rescue teams [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

The use of area extended particle swarm optimization (AEPSO) in swarm robotics

Atyabi

Powers

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Abstract-Swarm Robotics is the study of simple, un-intelligent robots teaming up together to address complicated tasks using cooperation and knowledge/skills sharing factors. Particle Swarm Optimization (PSO) is an Evolutionary algorithm inspired by animals' social behaviors. PSO has been used in various problems due to its fast convergence capability. Area Extended PSO (AEPSO) is an enhanced version of PSO designed to address complications in the Swarm Robotics field. These complications include dynamicity of the environment, degree of cooperation, time dependency of the tasks, and uncertain nature of the environment. This study investigates advantages and shortcomings of the AEPSO method in the robotic domain

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Learning to role-switch in multi-robot systems

Abstract: We present an approach that uses Q-learning

Cited by 27 publications

References 16 publications

Multi-agent role allocation: issues, approaches, and multiple perspectives

Multi-agent role allocation: issues, approaches, and multiple perspectives

An interaction abstraction model for seamless avatar exchange in CVET

The use of area extended particle swarm optimization (AEPSO) in swarm robotics

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