scholarly journals Evolution of Situated and Abstract Communication in Leader Selection and Borderline Identification Swarm Robotics Problems

2021 ◽  
Vol 11 (8) ◽  
pp. 3516
Author(s):  
Rafael Sendra-Arranz ◽  
Álvaro Gutiérrez
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Swarm Robotics ◽  
Direct Interaction ◽  
Identification Problem ◽  
Selection Task ◽  
Natural Evolution ◽  
Starting Conditions ◽  
Leader Selection

The design of robust yet simple communication mechanisms, that allow the cooperation through direct interaction among robots, is an important aspect of swarm robotics systems. In this paper, we analyze how an identical continuous-time recurrent neural network (CTRNN) controller can lead to the emergence of different kinds of communications within the swarm, either abstract or situated, depending on the problem to be faced. More precisely, we address two swarm robotics tasks that require, at some extent, communication to be solved: leader selection and borderline identification. The parameters of the CTRNN are evolved using separable natural evolution strategies. It is shown that, using the same starting conditions and robots’ controllers, the evolution process leads to the emergence of utterly diverging communications. Firstly, an abstract communication, in which the message carries all the information, results from evolution in the leader selection task. Alternatively, a purely situated communication, meaning that only the context is communicative, emerges when dealing with the borderline identification problem. Nonetheless, scalability and robustness properties are successfully validated.

scholarly journals Active Shape Discrimination with Compliant Bodies as Reservoir Computers

2016 ◽  
Vol 22 (2) ◽  
pp. 241-268 ◽  
Author(s):  
Chris Johnson ◽  
Andrew Philippides ◽  
Philip Husbands
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Continuous Time ◽  
Complex Dynamics ◽  
The Body ◽  
Control Problems ◽  
Reservoir Computing ◽  
Shape Discrimination ◽  
Reflexive Behavior ◽  
Processing Information

Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive.

scholarly journals Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 17-36
Author(s):  
Matteo Picozzi ◽  
Antonio Giovanni Iaccarino
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Identification Problem ◽  
Geothermal Field ◽  
Induced Earthquakes ◽  
Preparatory Phase ◽  
Seismic Networks ◽  
New Research ◽  
Natural Earthquakes ◽  
Large Earthquakes

Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.

scholarly journals Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

2021 ◽  
Author(s):  
Antonio Giovanni Iaccarino ◽  
Matteo Picozzi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Identification Problem ◽  
Geothermal Field ◽  
Induced Earthquakes ◽  
Preparatory Phase ◽  
Seismic Networks ◽  
New Research ◽  
Natural Earthquakes ◽  
Large Earthquakes

<p>Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a Recurrent Neural Network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.</p>

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