Engineering the Evolution of Self-Organizing Behaviors in Swarm Robotics: A Case Study

2011 ◽  
Vol 17 (3) ◽  
pp. 183-202 ◽  
Author(s):  
Vito Trianni ◽  
Stefano Nolfi
Keyword(s):  
Communication System ◽  
Swarm Robotics ◽  
A Priori ◽  
Evolutionary Process ◽  
Deep Understanding ◽  
Evolutionary Robotics ◽  
Powerful Approach ◽  
Large Groups ◽  
Self Organizing

Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.

scholarly journals Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity

2015 ◽  
Vol 21 (4) ◽  
pp. 464-480 ◽  
Author(s):  
Heiko Hamann
Keyword(s):  
Selective Pressure ◽  
A Priori ◽  
Evolutionary Robotics ◽  
Search Space ◽  
Alternative Methods ◽  
A Priori Knowledge ◽  
Behavioral Diversity ◽  
Definition Of ◽  
Priori Knowledge

Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance, which often includes task-specific features and hence a priori knowledge. Alternative methods, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.

scholarly journals Augmenting Scalable Communication-Based Role Allocation for a Three-Role Task

2020 ◽  
Vol 22 (64) ◽  
pp. 152-165
Author(s):  
Gustavo Martins ◽  
Paulo Urbano ◽  
Anders Lyhne Christensen
Keyword(s):  
Fitness Function ◽  
A Priori ◽  
Evolutionary Process ◽  
Evolutionary Robotics ◽  
Cognitive Architectures ◽  
Local Conditions ◽  
Fitness Functions ◽  
Signal Differentiation ◽  
Scalable Communication ◽  
Role Allocation

In evolutionary robotics role allocation studies, it is common that the role assumed by each robot is strongly associated with specific local conditions, which may compromise scalability and robustness because of the dependency on those conditions. To increase scalability, communication has been proposed as a means for robots to exchange signals that represent roles. This idea was successfully applied to evolve communication-based role allocation for a two-role task. However, it was necessary to reward signal differentiation in the fitness function, which is a serious limitation as it does not generalize to tasks where the number of roles is unknown a priori. In this paper, we show that rewarding signal differentiation is not necessary to evolve communication-based role allocation strategies for the given task, and we improve reported scalability, while requiring less a priori knowledge. Our approach for the two-role task puts fewer constrains on the evolutionary process and enhances the potential of evolving communication-based role allocation for more complex tasks. Furthermore, we conduct experiments for a three-role task where we compare two different cognitive architectures and several fitness functions and we show how scalable controllers might be evolved.

scholarly journals Revisiting objective tests: a case study in integration at honours level

2011 ◽  
Vol 8 (2) ◽  
Author(s):  
Pauline Maclaran ◽  
Alan Sangster
Keyword(s):  
United States ◽  
Multiple Choice ◽  
Deep Understanding ◽  
The United States ◽  
Multiple Choice Questions ◽  
Objective Tests ◽  
The Uk ◽  
Large Groups

Multiple-choice questions (MCQ) are widely accepted in the United States as a mode of assessment in undergraduate courses. In an environment of ever increasing class sizes, they provide a faster way to assess large groups of students, whilst also providing a way to measure deep understanding. However, in the UK there is scepticism from lecturers about the use of MCQ tests and, perhaps more significantly, from students themselves. This frequently means that someone using MCQs as a means of assessment will find both a lack of support from colleagues and resistance from students.DOI:10.1080/0968776000080207 

scholarly journals Wankelmut: A Simple Benchmark for the Evolvability of Behavioral Complexity

2021 ◽  
Vol 11 (5) ◽  
pp. 1994
Author(s):  
Thomas Schmickl ◽  
Payam Zahadat ◽  
Heiko Hamann
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
A Priori ◽  
Evolutionary Process ◽  
Evolutionary Robotics ◽  
Sensor Data ◽  
Control Software ◽  
Main Claim ◽  
Behavioral Complexity ◽  
Model Free

In evolutionary robotics, an encoding of the control software that maps sensor data (input) to motor control values (output) is shaped by stochastic optimization methods to complete a predefined task. This approach is assumed to be beneficial compared to standard methods of controller design in those cases where no a priori model is available that could help to optimize performance. For robots that have to operate in unpredictable environments as well, an evolutionary robotics approach is favorable. We present here a simple-to-implement, but hard-to-pass benchmark to allow for quantifying the “evolvability” of such evolving robot control software towards increasing behavioral complexity. We demonstrate that such a model-free approach is not a free lunch, as already simple tasks can be unsolvable barriers for fully open-ended uninformed evolutionary computation techniques. We propose the “Wankelmut” task as an objective for an evolutionary approach that starts from scratch without pre-shaped controller software or any other informed approach that would force the behavior to be evolved in a desired way. Our main claim is that “Wankelmut” represents the simplest set of problems that makes plain-vanilla evolutionary computation fail. We demonstrate this by a series of simple standard evolutionary approaches using different fitness functions and standard artificial neural networks, as well as continuous-time recurrent neural networks. All our tested approaches failed. From our observations, we conclude that other evolutionary approaches will also fail if they do not per se favor or enforce the modularity of the evolved structures and if they do not freeze or protect already evolved functionalities from being destroyed again in the later evolutionary process. However, such a protection would require a priori knowledge of the solution of the task and contradict the “no a priori model” approach that is often claimed in evolutionary computation. Thus, we propose a hard-to-pass benchmark in order to make a strong statement for self-complexifying and generative approaches in evolutionary computation in general and in evolutionary robotics specifically. We anticipate that defining such a benchmark by seeking the simplest task that causes the evolutionary process to fail can be a valuable benchmark for promoting future development in the fields of artificial intelligence, evolutionary robotics, and artificial life.

Tuning Transition Metal Carbides Activity by Surface Metal Alloying: Case Study on CO2 Capture and Activation

2018 ◽  
Author(s):  
Marti Lopez ◽  
Luke Broderick ◽  
John J Carey ◽  
Francesc Vines ◽  
Michael Nolan ◽  
...  
Keyword(s):  
Transition Metal ◽  
Co2 Capture ◽  
Density Functional ◽  
Deep Understanding ◽  
Metal Carbides ◽  
Transition Metal Carbides ◽  
Adsorption Sites ◽  
Surface Metal ◽  
A Minor

<div>CO2 is one of the main actors in the greenhouse effect and its removal from the atmosphere is becoming an urgent need. Thus, CO2 capture and storage (CCS) and CO2 capture and usage (CCU) technologies are intensively investigated as technologies to decrease the concentration</div><div>of atmospheric CO2. Both CCS and CCU require appropriate materials to adsorb/release and adsorb/activate CO2, respectively. Recently, it has been theoretically and experimentally shown that transition metal carbides (TMC) are able to capture, store, and activate CO2. To further improve the adsorption capacity of these materials, a deep understanding of the atomic level processes involved is essential. In the present work, we theoretically investigate the possible effects of surface metal doping of these TMCs by taking TiC as a textbook case and Cr, Hf, Mo, Nb, Ta, V, W, and Zr as dopants. Using periodic slab models with large</div><div>supercells and state-of-the-art density functional theory based calculations we show that CO2 adsorption is enhanced by doping with metals down a group but worsened along the d series. Adsorption sites, dispersion and coverage appear to play a minor, secondary constant effect. The dopant-induced adsorption enhancement is highly biased by the charge rearrangement at the surface. In all cases, CO2 activation is found but doping can shift the desorption temperature by up to 135 K.</div>

The Effects of an Aided Augmentative System on Communication and Speech in a Preschool Child with Developmental Delay: A Case Study

1999 ◽  
Vol 23 (1) ◽  
pp. 25-46
Author(s):  
Mark Carter
Keyword(s):  
Preschool Children ◽  
Developmental Delay ◽  
Communication System ◽  
Lower Proportion ◽  
Graphic Symbol ◽  
Symbol Use ◽  
Alternating Treatment Design ◽  
Treatment Design ◽  
Sustained Increase

The effect of an aided graphic augmentative communication system on communication and speech in a 4‐year 7‐month‐old child with developmental delay was evaluated in this case study. An alternating treatment design was used across 2 mealtime settings (morning tea and lunch) over a 6‐week period with counterbalancing of aided and unaided conditions. Once graphic symbol use emerged, there were slightly higher levels of unprompted communication and requests in the aided condition and a lower proportion of acts contained nonsymbolic communication. There was also a higher number of different words + symbols used in the aided condition. The introduction of the graphic symbols did not appear to have detrimental effects on speech but there was no evidence of sustained increase in frequency of speech or spoken vocabulary in the aided condition. Substantial increases were noted in the number of unprompted communicative turns and range of total vocabulary over the course of the study in both conditions. The need for further research on the effects of AAC on speech and communication in preschool children is highlighted.

Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. F25-F34 ◽  
Author(s):  
Benoit Tournerie ◽  
Michel Chouteau ◽  
Denis Marcotte
Keyword(s):  
Apparent Resistivity ◽  
A Priori ◽  
Shift Factor ◽  
Static Shift ◽  
Geostatistical Model ◽  
Data Set ◽  
Resistivity Sounding ◽  
The Difference ◽  
Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

Instrumental Means for Managing the Rational Behavior of Self-Organizing Autonomous Intelligent Agents

2021 ◽  
Vol 22 (4) ◽  
pp. 171-180
Author(s):  
V. B. Melekhin ◽  
M. V. Khachumov
Keyword(s):  
Intelligent Agents ◽  
Environmental Changes ◽  
A Priori ◽  
Operating Conditions ◽  
Basic Principles ◽  
Purposeful Behavior ◽  
Problem Solvers ◽  
Boundary Estimates ◽  
Self Learning ◽  
Self Organizing

We formulate the basic principles of constructing a sign-signal control for the expedient behavior of autonomous intelligent agents in a priori undescribed conditions of a problematic environment. We clarify the concept of a self-organizing autonomous intelligent agent as a system capable of automatic goal-setting when a certain type of conditional and unconditional signal — signs appears in a problem environment. The procedures for planning the expedient behavior of autonomous intelligent agents have been developed, that imitate trial actions under uncertainty in the process of studying the regularities of transforming situations in a problem environment, which allows avoiding environmental changes in the process of self-learning that are not related to the achievement of a given goal. Boundary estimates of the proposed procedures complexity for planning expedient behavior are determined, confirming the possibility of their effective implementation on the on-board computer of the automatic control system for the expedient activity of autonomous intelligent agents. We carry out an imitation on a personal computer of the proposed procedures for planning purposeful behavior, confirming the effectiveness of their use to build intelligent problem solvers for autonomous intelligent agents in order to endow them with the ability to adapt to a priori undescribed operating conditions. The main types of connections between various conditional and unconditional signal — signs of a problem environment are structured, which allows autonomous intelligent agents to adapt to complex a priori undescribed and unstable conditions of functioning.

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

Sign in / Sign up

Export Citation Format

Share Document