Peer Review #1 of "Modular automatic design of collective behaviors for robots endowed with local communication capabilities (v0.1)"

We investigate the automatic design of communication in swarm robotics through two studies. We first introduce Gianduja an automatic design method that generates collective behaviors for robot swarms in which individuals can locally exchange a message whose semantics is not a priori fixed. It is the automatic design process that, on a per-mission basis, defines the conditions under which the message is sent and the effect that it has on the receiving peers. Then, we extend Gianduja to Gianduja2 and Gianduja 3, which target robots that can exchange multiple distinct messages. Also in this case, the semantics of the messages is automatically defined on a per-mission basis by the design process. Gianduja and its variants are based on Chocolate, which does not provide any support for local communication. In the article, we compare Gianduja and its variants with a standard neuro-evolutionary approach. We consider a total of six different swarm robotics missions. We present results based on simulation and tests performed with 20 e-puck robots. Results show that, typically, Gianduja and its variants are able to associate a meaningful semantics to messages.

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Automatic modular design of robot swarms using behavior trees as a control architecture

PeerJ Computer Science ◽

10.7717/peerj-cs.314 ◽

2020 ◽

Vol 6 ◽

pp. e314

Author(s):

Antoine Ligot ◽

Jonas Kuckling ◽

Darko Bozhinoski ◽

Mauro Birattari

Keyword(s):

Swarm Robotics ◽

Modular Design ◽

Design Method ◽

Finite State Machines ◽

Control Architecture ◽

Automatic Design ◽

State Machines ◽

Collective Behaviors ◽

Behavior Trees ◽

Finite State

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions—into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple’s performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

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Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors

Applied Sciences ◽

10.3390/app10134654 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4654 ◽

Cited By ~ 2

Author(s):

David Garzón Ramos ◽

Mauro Birattari

Keyword(s):

Collective Behavior ◽

Swarm Robotics ◽

Design Method ◽

Fine Tuning ◽

Automatic Design ◽

Control Software ◽

Collective Behaviors ◽

Robot Swarms ◽

Software Modules ◽

Automatic Design Methods

Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

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Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms

Nature Communications ◽

10.1038/s41467-021-24642-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ken Hasselmann ◽

Antoine Ligot ◽

Julian Ruddick ◽

Mauro Birattari

Keyword(s):

Neural Networks ◽

Automatic Design ◽

Compelling Evidence ◽

Typical Application ◽

Collective Behaviors ◽

Evolutionary Methods ◽

Robot Swarms ◽

The Neural Networks ◽

Real Robot ◽

Line Design

AbstractNeuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms.

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Peer review of a dental curriculum: how one school did it

Journal of Dental Education ◽

10.1002/j.0022-0337.1976.40.11.tb01046.x ◽

1976 ◽

Vol 40 (11) ◽

pp. 761-762

Author(s):

PK Morse ◽

TR Dirksen

Keyword(s):

Peer Review ◽

Dental Curriculum

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Editorial: Peer review: A matter of expectations

Environmental Science & Technology ◽

10.1021/es00130a600 ◽

1984 ◽

Vol 18 (12) ◽

pp. 353-353

Keyword(s):

Peer Review

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An empirical review of gambling expansion and gambling-related harm

SUCHT - Zeitschrift für Wissenschaft und Praxis / Journal of Addiction Research and Practice ◽

10.1024/0939-5911/a000563 ◽

2018 ◽

Vol 64 (5-6) ◽

pp. 295-306 ◽

Cited By ~ 8

Author(s):

Debi A. LaPlante ◽

Heather M. Gray ◽

Pat M. Williams ◽

Sarah E. Nelson

Keyword(s):

Literature Review ◽

Peer Review ◽

Empirical Data ◽

Empirical Studies ◽

Expansion Method ◽

Responsible Gambling ◽

Review Literature ◽

Evidence Based ◽

Empirical Comparison ◽

Related Harm

Abstract. Aims: To discuss and review the latest research related to gambling expansion. Method: We completed a literature review and empirical comparison of peer reviewed findings related to gambling expansion and subsequent gambling-related changes among the population. Results: Although gambling expansion is associated with changes in gambling and gambling-related problems, empirical studies suggest that these effects are mixed and the available literature is limited. For example, the peer review literature suggests that most post-expansion gambling outcomes (i. e., 22 of 34 possible expansion outcomes; 64.7 %) indicate no observable change or a decrease in gambling outcomes, and a minority (i. e., 12 of 34 possible expansion outcomes; 35.3 %) indicate an increase in gambling outcomes. Conclusions: Empirical data related to gambling expansion suggests that its effects are more complex than frequently considered; however, evidence-based intervention might help prepare jurisdictions to deal with potential consequences. Jurisdictions can develop and evaluate responsible gambling programs to try to mitigate the impacts of expanded gambling.

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