In this study, we introduce an innovative approach to enhance interpretability in the design optimization of voxel-based soft robots (VSRs). VSRs present a unique challenge in achieving optimal designs due to their vast design space and the intricate relationships between individual voxels, compounded by the difficulty in interpreting the design choices and their functional implications. Traditional research has focused on meticulously adjusting grid voxels to optimize designs. However, this direct exploration of the vast design space often results in inconsistent outcomes and poses significant challenges to interpretation. To address these issues, we propose constraining the design space through part assembly and exploring designs using Bayesian optimization in a continuous feature space that offers higher interpretability. This approach facilitates diverse exploration and provides quantitative insights, marking a significant shift toward a more intuitive and exploratory design process in soft robotics. Our framework enables direct understanding of VSR designs, paving the way for future research and practical applications in this field.

To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. While these methods enhance performance, the application of collective intelligencebased approaches to complex network structures and the dynamics of agent interactions remain underexplored. This work extends the concept of multi-agent debate to more general network topologies, measuring the question-answering accuracy, influence, consensus, and the effects of bias on the collective. The results show that random networks perform similarly to fully connected networks despite using significantly fewer tokens. Furthermore, a strong consensus among agents correlates with correct answers, whereas divided responses typically indicate incorrect answers. Analysing the influence of the agents reveals a balance between selfreflection and interconnectedness; self-reflection aids when local interactions are incorrect, and local interactions aid when the agent itself is incorrect. Additionally, bias plays a strong role in system performance with correctly biased hub nodes boosting performance. These insights suggest that using random networks or scale-free networks with knowledgeable agents placed in central positions can enhance the overall question-answering performance of multi-agent systems.

Congestion control algorithms are used to help prevent congestion from occurring on the Internet. However, a definitive congestion control algorithm has yet to be developed. There are three reasons for this: First, the environment and usage of the Internet continue to evolve over time. Second, it is not clear what congestion control algorithms will be required as the environment evolves. Third, there is a limit to the number of the congestion control algorithms that can be developed by researchers. This paper proposes a method for automatically generating diverse congestion control algorithms and optimizing them in various environments by co-evolving network simulations as environments and congestion control algorithms as agents. In experiments conducted using co-evolution, although the algorithms generated were not on par with conventional practical congestion control algorithms, the intent of the procedures in the algorithms was interpretable from a human perspective. Furthermore, our results verify that it is possible to automatically discover a suitable environment for the evolution of a congestion control algorithm.

Against the background of remote work and labor-saving being promoted globally, the use of avatars is becoming widespread in our daily lives. Concurrently, the environments in which avatars are used are also diversifying, with environments appearing wherein communication is possible between humans and avatars as well as between avatars themselves. In this social situation, the effects of use of avatars on communication must be investigated. However, research to compare the effects of non-verbal information in avatar–avatar and human–avatar environments is inadequate. In this study, we created an avatar of which every facial feature can be moved independently and then measured the effects of facial expressions on communication in avatar–avatar and human–avatar environments. The results of a communication experiment based on negotiating in the context of the Prisoner's Dilemma game showed that mimicking facial expressions resulted in negotiations having a more cooperative outcome. Furthermore, the results suggest that this tendency is stronger in the avatar–avatar environment.

The emergence of novel forms in evolution can be viewed as the exploration of new regions in evolutionary space. This study investigates exploratory dynamics in evolutionary spaces through the empirical analysis of a social tagging system, which considers tags as an evolving entity and the tag set space as an evolutionary space. Dimensionality reduction showed distribution of a tag set in high dimensional tag set space embedded in 2-dimensional space and suggested that the new use of common tags was explored around common use, while new use of an uncommon tag was explored multi-regionally. Exploratory paths of evolution in tag set space were visualized as directed networks, and they exhibited structures called “branch” and “bunch.” The former suggests exploration deep into the space and the latter indicates wide exploration. These two modes of exploration imply exploration dynamics in evolutionary space in a mining-like manner in which prospects deposit novel tag sets by digging deep and excavating a wide area of strata with deposits.

Research is being conducted to understand human social interactions on the Web as a biological ecosystem. Keystone species in a biological ecosystem are defined as species that significantly impact the ecosystem if removed, irrespective of its low biomass. Identifying keystone species is an important issue, as they play a vital role in maintaining the entire ecosystem and its biodiversity. We hypothesize that a Web system is akin to an open, living ecological system that evolves and sustains itself by constantly updating its elements, which are sustained by the emergence of keystone species. We use data from an online bulletin board and identify keystone threads (”species”) that have a large impact if they are removed or become unpopular, despite their small population size. Our analysis confirms that keystone threads do exist in the system. The system seems to asymptotically evolve to a critical state. At the same time, the number of keystone species increases, and metabolism is enhanced. From a network topological perspective, the system evolves into a network with a high degree, closeness, and PageRank centralities. These findings suggest that keystone species play an important role in the evolution of online ecosystems. Further, by having keystone species, the system itself can decrease stability and bring about diversity to the ecosystem; consequently, the system can evolve.

Social network services (SNSs) are examples of non-living systems that evolve in response to internal and external events and have many similar characteristics assumed in biological evolution. In the present study, we analyzed the evolution of hashtag use on an SNS called RoomClip. Using a biological evolution analogy, we viewed each post (photo submission) as a species and each set of associated hashtags with a photo as genome. Further, we virtually defined parent–offspring relationships among posts based on their hashtag use and observed the resulting family tree of posts. Our analysis revealed that there was weak selection on hashtag usages relative to the Yule–Simon processes with strong feedback, and hashtag use quickly diverged. The evolution of novel hashtag combinations was observed, which is more salient than an evolution of individual novel hashtags.

The complete Proceedings of the The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE)

While technology has brought immeasurable benefits to humankind, recent advances in artificial intelligence and autonomous systems have also led to new ethical, legal, and social issues. We now face the problem of creating a cooperative society in which autonomous systems and people can coexist. The concept of artificial life provides unique perspectives, tools, and philosophies for furthering our understanding of complex living, lifelike, or hybrid systems. However, artificial life is still difficult to comprehend for those outside the academic community. We thus created a public co-creation community called ALIFE Lab, which aims to increase awareness of artificial life in collaboration with artificial life researchers and talents from creative fields such as design, art, and fashion. As one of the community activities, we organized a workshop-based program in which participants learned about Artificial Life and used it as a tool to conceive autonomous systems with concrete vocabulary and theory. This paper reports the methodology and outcomes of the workshop.