The Blessing and Curse of Emergence in Swarm Intelligence Systems

Harvey, J.D.

doi:10.1007/978-3-319-64816-3_6

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Firstly, human perception can be biased. For example, Harvey, Merrick and Abbass [2] identified regions in the parameter space for a boid model where swarming occurs according to mathematical metrics, while humans were unable to detect these regions. They demonstrated characteristic differences in the perception of swarming.…”

Section: A Modellingmentioning

confidence: 99%

“…Swarms are capable of emulating and augmenting multi-agent dynamic systems to complete intricate, objective-based behaviors with relatively simple local agents [1]. This manifestation of intelligent behavior without external regulation is described as an emergent property [2]. Swarm robotic systems permit decentralization of the control system, where each agent is responsible for its own processing and functionality, allowing simpler agents to be designed rather than an all-encompassing complex single agent [3].…”

Section: Introductionmentioning

confidence: 99%

“…Swarm robotic systems permit decentralization of the control system, where each agent is responsible for its own processing and functionality, allowing simpler agents to be designed rather than an all-encompassing complex single agent [3]. Therefore, decentralization makes robotic systems easier to build and program, as well as introducing robustness, flexibility, and scalability into the multi-robot system [2].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach

Long¹,

Sammut²,

Sgarioto³

et al. 2019

Preprint

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The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex systemlevel behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bioinspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. We present a comprehensive review of the literature on swarm shepherding to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.

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Section: A Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach

Long¹,

Sammut²,

Sgarioto³

et al. 2019

Preprint

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show abstract

“…As discussed in [1] designing models from swarm observation or learning them from swarm data, although a valid alternative, can be problematic since the human perception can be biased. For example [3] demonstrated characteristic differences in the perception of swarming that raise the question of whether models driven by human perception of swarming are biased or incomplete. Furthermore, there is no guarantee that the human-designed models reviewed in this paper would be the most efficient way to drive an artificial agent to shepherd, nor that it is the right and/or only way.…”

Section: Introductionmentioning

confidence: 99%

Obtaining emergent behaviors for swarm robotics singling with deep reinforcement learning

et al. 2023

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Isolating (singling) an individual from a group can be essential for protection, rescue or capture tasks. In this paper a system with multiple shepherds who must coordinate the sheep to achieve a specific singleness is proposed. We present a realistically modeled system that will be finally tested in a real robotic system. We want to encourage the adaptability of the system and provide different solutions by promoting the emergence of the swarm. In this line we will focus on the use of reinforcement learning, avoiding a manual design of the behavior in order to not restrict the resulting behaviors and to facilitate their adaptation. A detailed MDP model will be specified as well as the keys to reduce its dimensionality and facilitate its training.We will check the results of the obtained singling policy with respect to a greedy policy and focus on evaluating different behavioral strategies that can solve the problem in different ways. In addition, one of the obtained policies will be analyzed in detail to check both its robustness and its scalability with respect to the number of shepherds and sheep. This policy will be finally tested on a physical robotic swarm.

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