On the impact of body material properties on neuroevolution for embodied agents

Artificial agents required to perform non-trivial tasks are commonly controlled with Artificial Neural Networks (ANNs), which need to be carefully fine-tuned. This is where ANN optimization comes into play, often in the form of Neuroevolution (NE). Among artificial agents, the embodied ones, are characterized by a strong body-brain entanglement, i.e., a strong interdependence between the physical properties of the body and the controller. In this work, we aim at characterizing said interconnection, experimentally evaluating the impact body material properties have on NE for embodied agents. We consider the case of Voxel-based Soft Robots (VSRs), a class of simulated modular soft robots which achieve movement through the rhythmical contraction and expansion of their modules. We experiment varying several physical properties of VSRs and assess the effectiveness of the evolved controllers for the task of locomotion, together with their robustness and adaptability. Our results confirm the existence of a deep body-brain interrelationship for embodied agents, and highlight how NE fruitfully exploits the physical properties of the agents to give rise to a wide gamut of effective and adaptable behaviors

On the Mutual Influence of Human and Artificial Life: an Experimental Investigation

Our modern world is teeming with non-biological agents, whose growing complexity brings them so close to living beings that they can be cataloged as artificial creatures, i.e., a form of Artificial Life (ALife). Ranging from disembodied intelligent agents to robots of conspicuous dimensions, all these artifacts are united by the fact that they are designed, built, and possibly trained by humans taking inspiration from natural elements. Hence, humans play a fundamental role in relation to ALife, both as creators and as final users, which calls attention to the need of studying the mutual influence of human and artificial life. Here we attempt an experimental investigation of the reciprocal effects of the human-ALife interaction. To this extent, we design an artificial world populated by life-like creatures, and resort to open-ended evolution to foster the creatures adaptation. We allow bidirectional communication between the system and humans, who can observe the artificial world and voluntarily choose to perform positive or negative actions towards the creatures populating it; those actions may have a short- or long-term impact on the artificial creatures. Our experimental results show that the creatures are capable of evolving under the influence of humans, even though the impact of the interaction remains uncertain. In addition, we find that ALife gives rise to disparate feelings in humans who interact with it, who are not always aware of the importance of their conduct

Evolving modular soft robots without explicit inter-module communication using local self-attention

Modularity in robotics holds great potential. In principle, modular robots can be disassembled and reassembled in different robots, and possibly perform new tasks. Nevertheless, actually exploiting modularity is yet an unsolved problem: controllers usually rely on inter-module communication, a practical requirement that makes modules not perfectly interchangeable and thus limits their flexibility. Here, we focus on Voxel-based Soft Robots (VSRs), aggregations of mechanically identical elastic blocks. We use the same neural controller inside each voxel, but without any inter-voxel communication, hence enabling ideal conditions for modularity: modules are all equal and interchangeable. We optimize the parameters of the neural controller—shared among the voxels—by evolutionary computation. Crucially, we use a local self-attention mechanism inside the controller to overcome the absence of inter-module communication channels, thus enabling our robots to truly be driven by the collective intelligence of their modules. We show experimentally that the evolved robots are effective in the task of locomotion: thanks to self-attention, instances of the same controller embodied in the same robot can focus on different inputs. We also find that the evolved controllers generalize to unseen morphologies, after a short fine-tuning, suggesting that an inductive bias related to the task arises from true modularity

The Gaia mission

Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page. http://www.cosmos.esa.int/gai

Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies

Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies

Consumption and biochemical impact of commercially available plant-derived nutritional supplements. An observational pilot-study on recreational athletes

Abstract Background A growing consumption of natural (plant-derived) dietary supplements with ergogenic aims, with particular regard for ecdysteroids, phytoestrogens and vegetal sterols, has been registered over the last years among “recreational” athletes. The present study was carried out in order to evaluate the real knowledge of plant-derived nutritional supplements among physically active people as well as their real consumption. Additional aim was to evaluate the effects of these supplements on the health profile of the users. Methods Twenty-three trained subjects who habitually used natural dietary supplements, and 30 matched controls were analyzed for plasma biochemical markers and hormonal profile. Results The laboratory tests revealed the absence of any sign of organ toxicity/damage in both athletes and controls. On the contrary, hormone profiles revealed marked alterations in 15 (65%) out of the 23 of investigated athletes. Specifically, 10 males presented increased plasma levels of progesterone, 15 subjects presented abnormal estrogen levels, including 5 (2 F and 3 M) presenting a “dramatic” increased estrogen values and 2 two males with increased estrogen levels, increased testosterone levels and associated suppression of luteinizing hormone and follicle-stimulating hormone. Conclusions The results of the present study highlighted that the habitual consumption of plant-derived nutritional supplements is frequently associated with significant hormonal alterations both in male and female subjects. Although these biochemical alterations were not associated with signs or symptoms of organ toxicity/damage at the moment of the study, it cannot be excluded that, in the mid/long-term, these subjects would suffer of health problems secondary to chronic exposure to heavily altered hormonal levels. Further large scale studies are needed to confirm the results of this pilot study as well as to investigate the biological mechanisms at the base of the observed hormonal alterations.</p

Toxic Pseudo-nitzschia spp. in the northwestern Adriatic Sea: characterization of species composition by genetic and molecular quantitative analyses

Various genetic aspects of the toxic diatom Pseudo-nitzschia species in the northwestern Adriatic Sea were investigated. For this area, limited or no knowledge is available regarding the genetic diversity and geographical patterns of Pseudonitzschia spp., as well as the toxin content. Phylogenetic analyses identified strains belonging to P. delicatissima, P. calliantha, P. pungens and P. mannii. Networks of haplotypes inferred from Adriatic and worldwide strains revealed two main haplotypes in P. delicatissima from the Mediterranean and north Atlantic, with a single panmictic population in P. calliantha, and Adriatic P. pungens strains sharing the most frequent haplotype. The quantitative real-time PCR (qrt-PCR) assay was developed to estimate the number of rDNA copies and their variation among Pseudo-nitzschia species and strains. Qrt-PCR analysis showed that P. delicatissima and P. calliantha had different average rDNA copy numbers per cell (P, 0.001). It is suggested that different rDNA copy numbers among species might be used to discriminate between morphotypes identified using light microscopy. We also discuss how the rDNA copy number variability found among P. pungens strains from different months (P, 0.001) may relate to physiological activities and/or adaptative strategies. Northwestern Adriatic P. delicatissima strains produced domoic acid at low concentrations

Impact of Different Concentrations of Human Recombinant Growth Hormone on T Lymphocytes

The aim of the present study is to evaluate the effects induced by increasing concentrations of human recombinant growth hormone on T lymphocytes. Ten healthy volunteers and twelve subjects with symptomatic allergies were enrolled in the study. Peripheral blood mononuclear cells and purified T lymphocytes were cultured in the presence of graded concentrations of growth hormone. Following appropriate in vitro stimulations, the proportion of apoptotic T cells, the percentage of activated T lymphocyte subpopulations, the phytohemagglutinin responsiveness and the Th2 response were assessed by flow cytometry analysis. Moreover, in order to evaluate the phosphoinositol-3-kinase signaling pathway involvement, cells were also analyzed after treatment with LY294002. The treatment with different concentrations of growth hormone did not influence the activation pattern of un-stimulated T lymphocytes. On the contrary, growth hormone was able to modify the CD38/HLA-DR co-expression of T cells activated with phytohemoagglutinin. A different response was observed when samples obtained from healthy donors and from subjects with symptomatic allergies were analysed. Moreover, growth hormone treatment was able to increase the Th2 response in the samples obtained from healthy donors only. The results of the present study strongly support the hypothesis that growth hormone administration may play an important role in conditions of impaired/activated immune systems. The observation that growth hormone administration at high doses may reverse its effects and that it may promote a Th2-oriented response have significant clinical implications when considering the use of this hormone for artificially enhancing the physical performances of healthy athletes