6 research outputs found
Evolving Soft Robots with Vibration Based Movement
Creating eïŹective designs for soft robots is extremely diïŹcult due to the large number of diïŹerent possibilities for shape, material properties, and movement mechanisms. Due to the lack of methods to design soft robots, previous research has used evolutionary algorithms to tackle this problem of overwhelming options. A popular technique is to use generative encodings to create designs using evolutionary algorithms because of their modularity and ability to induce large scale coordinated change. The main drawback of generative encodings is that it is diïŹcult to know where along the ontogenic trajectory resides the phenotype with the highest ïŹtness. The two main approaches for addressing this issue are static and scaled developmental timings. In order to compare the eïŹectiveness of each of these two approaches, I have implemented a framework capable of evolving soft robot designs that utilize vibration as a movement mechanism
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conwayâs life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MRâs applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithmsâ performance on Amazonâs Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
SpĂ©ciation guidĂ©e par l'environnementâ : interactions sur des pĂ©riodes Ă©volutionnaires de communautĂ©s de plantes artificielles
Depuis des dĂ©cades, les chercheurs en Vie Artificielle on crĂ©Ă© une plĂ©thore de crĂ©atures en utilisant de multiples schĂ©mas dâencodage, capacitĂ©s motrices et aptitudes cognitives. Un motif rĂ©current, cependant, est que la focalisation est centrĂ©e sur les individus Ă Ă©voluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus Ă©volutionnaire vers des rĂ©gions de lâespace gĂ©nĂ©tique plus robustes and diverses. Nous avons conçu un modĂšle morphologique complexe, basĂ© sur les graphes orientĂ©s de K. Sims, qui repose sur le moteur physique Bullet pour la prĂ©cision et utilise des contraintes Ă 6 DegrĂ©s de LibertĂ© pour connecter les paires dâorganes. Nous avons ainsi Ă©voluĂ© un panel de plantes Ă lâaspect naturel qui devaient survivre malgrĂ© des niveaux de ressources variables induits par une source de lumiĂšre mobile et des motifs de pluies saisonniĂšres. En plus de cette expĂ©rience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins dâherbe statiques. La complexitĂ© de ce modĂšle, cependant, ne permettait pas la mise a lâĂ©chelle dâune Ă©volution de populations et a donc Ă©tĂ© rĂ©duit dans lâexpĂ©rience suivante, notamment en supprimant le moteur physique. Cela nous a amenĂ© Ă lâexploration de la co-Ă©volution de populations composĂ©es dâune unique espĂšce et ayant la capacitĂ© de se reproduire de maniĂšre autonome grĂące Ă notre Bail-Out Crossover (Croisement avec DĂ©sistement). Bien que les populations rĂ©sultantes nâont pas dĂ©montrĂ© un grand intĂ©rĂȘt pour cette aptitude, elles ont nĂ©anmoins fourni dâimportantes informations sur les mĂ©canismes dâauto-reproduction. Ceux-ci ont Ă©tĂ© mis en action dans un second modĂšle inspirĂ© des travaux de Bornhofen. GrĂące Ă sa lĂ©gĂšretĂ©, cela nous a permis de traiter non seulement de plus grandes populations (de lâordre de milliers dâindividus) mais aussi de plus longues pĂ©riodes Ă©volutionnaires (100 annĂ©es, approximativement 5000 gĂ©nĂ©rations). Notre premiĂšre expĂ©rience avec ce modĂšle sâest concentrĂ©e sur la possibilitĂ© de reproduire des cas dâĂ©cole de spĂ©ciation (allopatrique, parapatrique, pĂ©ripatrique) sur cette plate-forme. GrĂące Ă APOGet, une nouvelle procĂ©dure de regroupement pour lâextraction en parallĂšle dâespĂšces Ă partir dâun arbre gĂ©nĂ©alogique, nous avons pu affirmer que le systĂšme Ă©tait effectivement capable de spĂ©ciation spontanĂ©e. Cela nous a conduit Ă une derniĂšre expĂ©rience dans laquelle lâenvironnement Ă©tait contrĂŽlĂ© par de la Programmation GĂ©nĂ©tique CartĂ©sienne (CGP), permettant ainsi une Ă©volution automatique dâune population et des contraintes abiotiques auxquelles elle Ă©tait confrontĂ©e. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survĂ©cu Ă de brutales et imprĂ©visibles variations environnementales. En les comparant Ă un groupe contrĂŽle c pour lequel les contraintes ont Ă©tĂ© maintenues faibles et constantes, le groupe Ă©voluĂ© e a montrĂ© des performances mitigĂ©es: dans les deux types de tests, une moitiĂ© de e surpassait c qui, Ă son tour, surpassait la moitiĂ© restante de e. Nous avons aussi trouvĂ© une trĂšs forte corrĂ©lation entre les chutes catastrophiques de population et la performance des Ă©volutions correspondantes. Il en rĂ©sulte que lâĂ©volution de population dans des environnements hostiles et dynamiques nâest pas une panacĂ©e bien que ces expĂ©riences en dĂ©montrent le potentiel et souligne le besoin dâĂ©tudes ultĂ©rieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Simsâ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life
SpĂ©ciation guidĂ©e par l'environnementâ : interactions sur des pĂ©riodes Ă©volutionnaires de communautĂ©s de plantes artificielles
Depuis des dĂ©cades, les chercheurs en Vie Artificielle on crĂ©Ă© une plĂ©thore de crĂ©atures en utilisant de multiples schĂ©mas dâencodage, capacitĂ©s motrices et aptitudes cognitives. Un motif rĂ©current, cependant, est que la focalisation est centrĂ©e sur les individus Ă Ă©voluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus Ă©volutionnaire vers des rĂ©gions de lâespace gĂ©nĂ©tique plus robustes and diverses. Nous avons conçu un modĂšle morphologique complexe, basĂ© sur les graphes orientĂ©s de K. Sims, qui repose sur le moteur physique Bullet pour la prĂ©cision et utilise des contraintes Ă 6 DegrĂ©s de LibertĂ© pour connecter les paires dâorganes. Nous avons ainsi Ă©voluĂ© un panel de plantes Ă lâaspect naturel qui devaient survivre malgrĂ© des niveaux de ressources variables induits par une source de lumiĂšre mobile et des motifs de pluies saisonniĂšres. En plus de cette expĂ©rience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins dâherbe statiques. La complexitĂ© de ce modĂšle, cependant, ne permettait pas la mise a lâĂ©chelle dâune Ă©volution de populations et a donc Ă©tĂ© rĂ©duit dans lâexpĂ©rience suivante, notamment en supprimant le moteur physique. Cela nous a amenĂ© Ă lâexploration de la co-Ă©volution de populations composĂ©es dâune unique espĂšce et ayant la capacitĂ© de se reproduire de maniĂšre autonome grĂące Ă notre Bail-Out Crossover (Croisement avec DĂ©sistement). Bien que les populations rĂ©sultantes nâont pas dĂ©montrĂ© un grand intĂ©rĂȘt pour cette aptitude, elles ont nĂ©anmoins fourni dâimportantes informations sur les mĂ©canismes dâauto-reproduction. Ceux-ci ont Ă©tĂ© mis en action dans un second modĂšle inspirĂ© des travaux de Bornhofen. GrĂące Ă sa lĂ©gĂšretĂ©, cela nous a permis de traiter non seulement de plus grandes populations (de lâordre de milliers dâindividus) mais aussi de plus longues pĂ©riodes Ă©volutionnaires (100 annĂ©es, approximativement 5000 gĂ©nĂ©rations). Notre premiĂšre expĂ©rience avec ce modĂšle sâest concentrĂ©e sur la possibilitĂ© de reproduire des cas dâĂ©cole de spĂ©ciation (allopatrique, parapatrique, pĂ©ripatrique) sur cette plate-forme. GrĂące Ă APOGet, une nouvelle procĂ©dure de regroupement pour lâextraction en parallĂšle dâespĂšces Ă partir dâun arbre gĂ©nĂ©alogique, nous avons pu affirmer que le systĂšme Ă©tait effectivement capable de spĂ©ciation spontanĂ©e. Cela nous a conduit Ă une derniĂšre expĂ©rience dans laquelle lâenvironnement Ă©tait contrĂŽlĂ© par de la Programmation GĂ©nĂ©tique CartĂ©sienne (CGP), permettant ainsi une Ă©volution automatique dâune population et des contraintes abiotiques auxquelles elle Ă©tait confrontĂ©e. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survĂ©cu Ă de brutales et imprĂ©visibles variations environnementales. En les comparant Ă un groupe contrĂŽle c pour lequel les contraintes ont Ă©tĂ© maintenues faibles et constantes, le groupe Ă©voluĂ© e a montrĂ© des performances mitigĂ©es: dans les deux types de tests, une moitiĂ© de e surpassait c qui, Ă son tour, surpassait la moitiĂ© restante de e. Nous avons aussi trouvĂ© une trĂšs forte corrĂ©lation entre les chutes catastrophiques de population et la performance des Ă©volutions correspondantes. Il en rĂ©sulte que lâĂ©volution de population dans des environnements hostiles et dynamiques nâest pas une panacĂ©e bien que ces expĂ©riences en dĂ©montrent le potentiel et souligne le besoin dâĂ©tudes ultĂ©rieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Simsâ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life
Visual strategies underpinning social cognition in traumatic brain injury
Impairments in social cognition after traumatic brain injury (TBI) are well documented but
poorly understood (McDonald, 2013). Deficits in emotion perception, particularly facial affect
recognition, are frequently reported in the literature (Babbage et al., 2011; Knox & Douglas,
2009), as well as mentalizing impairments and difficulty in understanding sincere and sarcastic
exchanges (Channon, Pellijeff & Rule, 2005). To fully understand social impairments, both
low-level and high-level processes must be explored. Few studies have focused on low-level
perceptual processes in regards to facial affect recognition after TBI, and those that do typically
use static social stimuli which lack ecological validity (Alves, 2013). This thesis employed eyetracking technology to explore the visual strategies underpinning the processing of
contemporary static and dynamic social cognition tasks in a group of 18 TBI participants and
18 age, gender and education matched controls.
The group affected by TBI scored significantly lower on the Movie for the Assessment of
Social Cognition (MASC; Dziobek, et al., 2006), the Amsterdam Dynamic Facial Expression
Set (ADFES; van der Schalk, Hawk, Fischer & Doosje, 2009), and The Assessment of Social
Inference Test (McDonald et al., 2003). These findings suggest that, across a range of reliable
assessments, individuals with TBI displayed significant social cognition deficits, including
emotion perception and theory of mind, thus presenting strong evidence that social cognition
is altered post-TBI. Impairments were not related to low-level visual processing as measured
through eye-tracking metrics. This important insight suggests that social cognition changes
post-TBI is likely associated with impairments in higher-level cognitive functioning.
Interestingly, the group with TBI did display some aberrant fixation patterns in response to one
static and one dynamic task but gaze patterns were similar between the groups on the remaining
tasks. These non-uniform results warrant further exploration of low-level alterations post-TBI.
Findings are discussed in reference to academic and clinical implications