Evolutionary robotics: model or design?

In this paper, I review recent work in evolutionary robotics (ER), and discuss the perspectives and future directions of the field. First, I propose to draw a crisp distinction between studies that exploit ER as a design methodology on the one hand, and studies that instead use ER as a modeling tool to better understand phenomena observed in biology. Such a distinction is not always that obvious in the literature, however. It is my conviction that ER would profit from an explicit commitment to one or the other approach. Indeed, I believe that the constraints imposed by the specific approach would guide the experimental design and the analysis of the results obtained, therefore reducing arbitrary choices and promoting the adoption of principled methods that are common practice in the target domain, be it within engineering or the life sciences. Additionally, this would improve dissemination and the impact of ER studies on other disciplines, leading to the establishment of ER as a valid tool either for design or modeling purposes

On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches

This study compares two different evolutionary approaches (clonal and aclonal) to the design of homogeneous two-robot teams (i.e. teams of morphologically identical agents with identical controllers) in a task that requires the agents to specialise to different roles. The two approaches differ mainly in the way teams are formed during evolution. In the clonal approach, a team is formed from a single genotype within one population of genotypes. In the aclonal approach, a team is formed from multiple genotypes within one population of genotypes. In both cases, the goal is the synthesis of individual generalist controllers capable of integrating role execution and role allocation mechanisms for a team of homogeneous robots. Our results diverge from those illustrated in a similar comparative study, which supports the superiority of the aclonal versus the clonal approach. We question this result and its theoretical underpinning, and we bring new empirical evidence showing that the clonal outperforms the aclonal approach in generating homogeneous teams required to dynamically specialise for the benefit of the team. The results of our study suggest that task-specific elements influence the evolutionary dynamics more than the genetic relatedness of the team members. We conclude that the appropriateness of the clonal approach for role allocation scenarios is mainly determined by the specificity of the collective task, including the evaluation function, rather than by the way in which the solutions are evaluated during evolution

Towards a Theological Overcoming of Anthropocentrism. The Vegetarian Choice

The article intends to demonstrate that a theology of vegetarianism is possible, despite some contrary evidence present in the biblical texts. Like other theologies dealing with issues not directly voiced in the Bible, it becomes possible to interpret the biblical statements in a new way, on the bases of a specific methodology. As a result, an objective comprehension will go back inductively to Sacred Scripture. The article advocates for applying this new method as well as for introducing its ethical implications into the Christian tradition. An additional supportive argument in favour of establishing the new understanding can be found in the history of the Roman Church, besides the consolidated custom of carnivorous nutrition: there has been no shortage of positions in favour of vegetarian asceticism. This stance was also represented by Thomas Aquinas. By valorizing classic Christian authors in favour of vegetarianism (starting with Jerome), the inauguration of the theology of vegetarianism becomes legitimised. Such an inauguration would reorient Christian thought toward reconsidering cosmology, ecology and topical contemporary issues such as anthropocentrism and speciesism.MNiSW grant 261/ WCN/2019/1 “Wsparcie dla Czasopism Naukowych

Swarm Cognition and Artificial Life

Abstract. Swarm Cognition is the juxtaposition of two relatively un-related concepts that evoke, on the one hand, the power of collective behaviours displayed by natural swarms, and on the other hand the com-plexity of cognitive processes in the vertebrate brain. Recently, scientists from various disciplines suggest that, at a certain level of description, op-erational principles used to account for the behaviour of natural swarms may turn out to be extremely powerful tools to identify the neuroscien-tific basis of cognition. In this paper, we review the most recent studies in this direction, and propose an integration of Swarm Cognition with Artificial Life, identifying a roadmap for a scientific and technological breakthrough in Cognitive Sciences.

Damage Detection from SAR Imagery: Application to the 2003 Algeria and 2007 Peru Earthquakes

This paper is focused on the improvement and further validation of a recently proposed approach for the joint use of radar satellite imagery of an area affected by a major disaster and ancillary data. The study was carried out at different sites on imagery of two different earthquakes occurred one in the Mediterranean coast of Algeria on May 21st, 2003, which severely affected the city of Boumerdes, and one in the Pacific Coast of Peru on August, 15th, 2007. The combination of different radar-extracted features results in very fuzzy classification of the damage patterns, far less detailed than what available using optical imagery. However, focused results using the above-mentioned ancillary data provide enough detail and precision to be comparable with them. In particular, quantized damage level at the block level is achieved at enough detail using ALOS/PALSAR data and thus validates the original idea

Field coverage and weed mapping by UAV swarms

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols. © 2017 IEEE