Equal but different: Task allocation in homogeneous communicating robots

Gigliotta, Onofrio

doi:10.1016/j.neucom.2017.05.093

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…In this regard, a sequential and parallel delivery services composition using a swarm of drones was proposed [1]. While drone swarms in delivery represent a major advancement, developing swarm allocation methods is essential to unlock their full potential and obtain teamwork benefit [10].…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

Alkouz¹,

Bouguettaya²

2021

Preprint

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We propose a novel framework for the re-allocation of drone swarms for delivery services known as Swarm-based Drone-as-a-Service (SDaaS). The re-allocation framework ensures maximum profit to drone swarm providers while meeting the time requirement of service consumers. The constraints in the delivery environment (e.g., limited recharging pads) are taken into consideration. We utilize reinforcement learning (RL) to select the best allocation and scheduling of drone swarms given a set of requests from multiple consumers. We conduct a set of experiments to evaluate and compare the efficiency of the proposed approach considering the provider's profit and run-time efficiency.

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Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

Alkouz¹,

Bouguettaya²

2021

Preprint

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“…If robots are to be incorporated into a social system, scientists need to ensure that robots conform to the same social expectations and interpersonal dependence as other members of the team. As robots become more complex and adaptive, there is greater potential for them to adapt and carry out the required communication necessary to convey and receive information from humans and create new response patterns that match the needs of the environment (Gigliotta, 2018). If robots are to fill a new niche in human culture, they, too, need to be adaptive and find unique ways of communicating with humans that fit with evolved cooperative behavioral strategies already in place (Gigliotta, 2018).…”

mentioning

confidence: 99%

“…As robots become more complex and adaptive, there is greater potential for them to adapt and carry out the required communication necessary to convey and receive information from humans and create new response patterns that match the needs of the environment (Gigliotta, 2018). If robots are to fill a new niche in human culture, they, too, need to be adaptive and find unique ways of communicating with humans that fit with evolved cooperative behavioral strategies already in place (Gigliotta, 2018). This kind of automated adaptability is essential for continuing to provide immediate behavioral and environmental feedback needed to train or shape behaviors in the animal or human subject.…”

mentioning

confidence: 99%

Robots Can Train Humans Using Principles of Operant Conditioning Through Visual Reinforcement Tools

Kieson¹,

Maske²,

Abramson³

et al. 2018

IJCP

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Researchers have established new techniques to study human-robot interactions based on current knowledge in interspecies communication and comparative psychology. Studies on animal acceptance of robot conspecifics in complex social environments has led to the development of robots that adapt to animal and human behaviors. Using a robot with adaptable algorithms developed by the authors, the researchers hypothesized that, by using familiar visual rewards as positive reinforcement, robots could use operant conditioning principles to teach humans a basic task. The robot in this study independently determines optimal control of construction equipment by capturing the motions from an expert operator. The robot then attempts to teach those same skills to novice operators using familiar, yet simple, visual reinforcement tools. In this study, participants were asked to manipulate a model excavator using feedback from the guidance system on a nearby computer screen. Participants were randomly assigned to one of three groups: simple visual reinforcement, complex guidance, and no visual feedback (blank screen). To measure learning, participants returned a day later to repeat the task without the guidance. The group using simple feedback resulted in cycle times that were closer to the expert times than both the complex or control groups and were significantly different end times (p < .05) than either group. This result supports our hypothesis that, similar to what’s been found in vertebrates and invertebrates, robots can shape behaviors of humans using visual positive reinforcement.

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A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

Alkouz

Bouguettaya

2021

Service-Oriented Computing

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Equal but different: Task allocation in homogeneous communicating robots

Cited by 8 publications

References 17 publications

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

Robots Can Train Humans Using Principles of Operant Conditioning Through Visual Reinforcement Tools

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

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