Costs and benefits of behavioral specialization

Brutschy, Arne; Tran, Nam-Luc; Baiboun, Nadir; Frison, Marco; Pini, Giovanni; Roli, Andrea; Dorigo, Marco; Birattari, Mauro

doi:10.1016/j.robot.2012.05.018

Cited by 16 publications

(5 citation statements)

References 19 publications

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“…Learning is an example of behavioral plasticity. For example, if one simulates improved task performance through repetition there can be emergent task specialization (Brutschy et al, 2012). Task sequencing has been demonstrated at run-time without prior knowledge of the correct ordering, demonstrating a form of reinforcement learning, albeit with abstractions of the tasks themselves (Garattoni and Birattari, 2018).…”

Section: Learning (On-deployment)mentioning

confidence: 99%

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Hunt

2020

Front. Robot. AI

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The real world is highly variable and unpredictable, and so fine-tuned robot controllers that successfully result in group-level "emergence" of swarm capabilities indoors may quickly become inadequate outside. One response to unpredictability could be greater robot complexity and cost, but this seems counter to the "swarm philosophy" of deploying (very) large numbers of simple agents. Instead, here I argue that bioinspiration in swarm robotics has considerable untapped potential in relation to the phenomenon of phenotypic plasticity: when a genotype can produce a range of distinctive changes in organismal behavior, physiology and morphology in response to different environments. This commonly arises following a natural history of variable conditions; implying the need for more diverse and hazardous simulated environments in offline, pre-deployment optimization of swarms. This will generate-indicate the need for-plasticity. Biological plasticity is sometimes irreversible; yet this characteristic remains relevant in the context of minimal swarms, where robots may become mass-producible. Plasticity can be introduced through the greater use of adaptive threshold-based behaviors; more fundamentally, it can link to emerging technologies such as smart materials, which can adapt form and function to environmental conditions. Moreover, in social animals, individual heterogeneity is increasingly recognized as functional for the group. Phenotypic plasticity can provide meaningful diversity "for free" based on early, local sensory experience, contributing toward better collective decision-making and resistance against adversarial agents, for example. Nature has already solved the challenge of resilient self-organisation in the physical realm through phenotypic plasticity: swarm engineers can follow this lead.

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Section: Learning (On-deployment)mentioning

confidence: 99%

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Hunt

2020

Front. Robot. AI

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“…In collaborative artificial systems, such as social multi-agent systems ( (27) among others . This specialization aims to provide greater efficiency for the artificial system, so that there is an improvement in the achievement of its overall aims.…”

Section: Related Workmentioning

confidence: 99%

Improving Intelligent Systems: Specialization

Román

Rodrı́guez

Corchado

2014

Communications in Computer and Information Science

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The specialization exists in biological systems and in human organizations, as a methodology to improve processes and optimize their aims. This specialization in artificial intelligent systems such as multi-agent systems, can improve their aims, depending on the type of specialization and the goals which they need to achieve. The enterprise networks are a collaboration model between companies which we can apply over these intelligent systems, so that, these systems can achieve more complex aims. Therefore, in this collaboration type, is necessary to consider their specialization type, and how they could collaborate to achieve aims, that by themselves would not be possible.

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“…One approach for utilising multiple robots in this way is semi-autonomous operation supervised by a small number of professionals; only requiring human experts for crucial decisions [8]. Current commercial UAV platforms also allow the deployment of a diverse group of robots, allowing them to combine their individual capabilities to be more efficient [9]. For example, fixed-wing UAVs are capable of flying faster and carrying larger payloads, but when they do so, they should be deployed with higher safety measures (safety pilots are required for non-lightweight aircraft).…”

Section: Introductionmentioning

confidence: 99%

Collaborative online planning for automated victim search in disaster response

Beck

Teacy

Rogers

et al. 2018

Robotics and Autonomous Systems

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Collaboration is essential for effective performance by groups of robots in disaster response settings. Here we are particularly interested in heterogeneous robots that collaborate in complex scenarios with incomplete, dynamically changing information. In detail, we consider an automated victim search setting, where unmanned aerial vehicles (UAVs) with different capabilities work together to scan for mobile phones and find and provide information about possible victims near these phone locations. The state of the art for such collaboration is robot control based on independent planning for robots with different tasks and typically incorporates uncertainty with only a limited scope. In contrast, in this paper, we take into account complex relations between robots with different tasks. As a result, we create a joint, full-horizon plan for the whole robot team by optimising over the uncertainty of future information gain using an online planner with hindsight optimisation. This joint plan is also used for further optimisation of individual UAV paths based on the long-term plans of all robots. We evaluate our planner’s performance in a realistic simulation environment based on a real disaster and find that our approach finds victims 25% faster compared to current state-of-the-art approaches

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Costs and benefits of behavioral specialization

Cited by 16 publications

References 19 publications

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Improving Intelligent Systems: Specialization

Collaborative online planning for automated victim search in disaster response

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