This paper introduces Alter3, a humanoid robot that demonstrates spontaneous motion generation through the integration of GPT-4, Large Language Model (LLM). This overcomes challenges in applying language models to direct robot control. By translating linguistic descriptions into actions, Alter3 can autonomously perform various tasks. The key aspect of humanoid robots is their ability to mimic human movement and emotions, allowing them to leverage human knowledge from language models. This raises the question of whether Alter3+GPT-4 can develop a “minimal self” with a sense of agency and ownership. This paper introduces mirror self-recognition and rubber hand illusion tests to assess Alter3’s potential for a sense of self. The research suggests that even disembodied language models can develop agency when coupled with a physical robotic platform.

We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies. By leveraging real-world data, the environment simulates a target ant trail that a controllable agent must learn to replicate, using sensory data observed by the target ant. This work aims to contribute to the neuroevolution of models for collective behaviour, focusing on evolving neural architectures that encode domain-specific behaviours in the network topology. By evolving models that can be modified and studied in a controlled environment, we can uncover the necessary conditions required for collective behaviours to emerge. We hope this environment will be useful to those studying the role of interactions in emergent behaviour within collective systems.

This study applies the information-theoretic measure of Non- Trivial Information Closure (NTIC) to quantify the autonomy of individual ants within a colony. We calculate the degree to which an ant’s future behavior is determined by its own past states versus being influenced by its local environment. Results show that individual ants exhibit consistent levels of autonomy across different timescales. This suggests that ant behavior reflects a non-trivial processing of both internal and external information, rather than being a simple reflexive response to stimuli. The approach demonstrates the utility of NTIC as a metric for assessing autonomy in complex biological systems. These findings lay the groundwork for future studies of autonomy and information processing in swarms.

The relationship between reaction-diffusion (RD) systems, characterized by continuous spatiotemporal states, and cellular automata (CA), marked by discrete spatiotemporal states, remains poorly understood. This paper delves into this relationship through an examination of a recently developed CA known as Lenia. We demonstrate that asymptotic Lenia, a variant of Lenia, can be comprehensively described by differential equations, and, unlike the original Lenia, it is independent of time-step ticks. Further, we establish that this formulation is mathematically equivalent to a generalization of the kernel-based Turing model (KT model). Stemming from these insights, we establish that asymptotic Lenia can be replicated by an RD system composed solely of diffusion and spatially local reaction terms, resulting in the simulated asymptotic Lenia based on an RD system, or “RD Lenia”. However, our RD Lenia cannot be construed as a chemical system since the reaction term fails to satisfy mass-action kinetics.

Artificial intelligence (AI) has seen drastic advances given recently powerful models with astonishing individual capacities, while the biological evolutionary strategy focuses more on collective intelligence. We seek to bridge biological collective intelligence with artificial intelligence, by studying collective motion of agents, inspired by the biological ants collectively solving tasks while using chemical pheromone for communication. We train agent in a single setting to acquire chemotaxis and duplicate the trained agents to form a population. We observe several interesting dynamics where collective intelligence is realized in our AI models, and expect to further analyze the impact of communication on collective dynamics.

We experimentally and numerically delve into the life-like behavior of an oil droplet in an aqueous surfactant solution in response to changes in the volume and composition ratio of the droplet. Much research has been dedicated to investigating living and non-living systems independently, albeit the boundary between the two remains unclear. To address this issue, we conducted experimental observations and identified several types of spontaneous motion exhibited by the oil droplet, which varied depending on its parameters. We then quantified the characteristic motion patterns utilizing analysis from multiple aspects and compared the differences between oil droplets - as an example of non-living material -and Tetrahymena thermophila - as a living system. Furthermore, in an attempt to reveal the deterministic or stochastic rule governing each system, a numerical simulation of the Langevin equations was performed.

We investigated the effect of time delay on hand motion and subjective time perception in a virtual environment. Our results indicated that time delays correlated with decreased hand motion speed and altered time perception. These findings suggest that body movements act as critical reference points for constructing subjective time, and alterations in these references can influence time perception.

We conducted a comprehensive tracking study of 64 unmarked ants within the same arena to examine the dynamics of individual behaviors within a collective, aiming to understand the underlying mechanisms that drive the colony's collective behaviors. Specifically, we analyzed the movement patterns of the ants to identify the “algorithm” governing their actions. One such approach we employed is the ϵ -machine method, pioneered by Crutchfield and colleagues, which predicts motion using a stochastic finite state machine. The results of our study revealed that individual ants exhibited either deterministic or stochastic behaviors, contingent upon their roles within the colony. Ants contributing to cluster formation displayed deterministic behaviors, whereas those exploring outside of the cluster were more likely to demonstrate stochastic behaviors.

In this study, we introduce a novel system whereby a humanoid robot, named Alter3, employs a selective combination of three strategies - Mimicking, Imitation, and Dream - to replicate human behavior observed through its camera-based eyes. This work builds upon previous research [Masumori et al. (2021); Ikegami et al. (2021)]. In Mimicking mode, Alter3 recreates “how” a human moves by calculating joint angles. In Imitation mode, it identifies and reproduces symbolic poses through a pre-trained Variational AutoEncoder (VAE), essentially replicating “what” the human did. When imitation proves unsuccessful, Alter3 engages its Dream mode, where it recalls altered memories through selection and mutation processes, allowing it to generate movements based on experience. Moreover, in the absence of a human subject, Alter3, with its eyes closed, retrieves and performs movements from memory. Our findings reveal that the concurrent use of the three strategies (Mimicking, Imitation, Dreaming) stabilizes the latent space state and optimizes the range of identifiable poses. Furthermore, the behavior that Alter3 generates through Dream mode evolves from symbolic movements via the Imitation pathway. These findings suggest that new movements can be created from concept-based motions by selectively employing both methodical (Mimicking) and symbolic (Imitation) motions.

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.

In this study, we report the investigations conducted on the mimetic behavior of a new humanoid robot called Alter3. Alter3 autonomously imitates the motions of a human in front of him and stores the motion sequences in its memory. Alter3 also contains a self-simulator that simulates its own motions before executing them and generates a self-image. We investigate how this mimetic behavior evolves with human interaction, by analyzing memory dynamics and information flow between Alter3 and humans. One important observation from this study is that when Alter3 fails to imitate human motion, humans tend to imitate Alter3 instead. This tendency is quantified by the alternation of the direction of information flow. At the conference we will also report on the experiments we carried out recently, in which two Alters imitated each other, and in which we let people possess and imitate Alter.

We present a novel artificial cognitive map system using the generative deep neural networks called Variational Autoencoder / Generative Adversarial Network (VAE/GAN), which encodes input images into the latent space and the structure of the latent space is self-organized through the learning. Our results show that the distance of the predicted image is reflected in the distance of the corresponding latent vector after training, which indicates that the latent space is organized to reflect the proximity structure of the dataset. This system is also able to internally generate temporal sequences analogous to hippocampal replay/pre-play, and we found that these sequences are not just the exact replay of the past experience, and this could be the origin of creating novel sequences from the past experiences. Having this generative nature of cognition is thought as a prerequisite for artificial cognitive systems.

Acoustic ecologist Bernie Krause hypothesized that rich soundscapes in mature ecosystems are generated by sound communication between different species with differentiating acoustic niches. This hypothesis, called the acoustic niche hypothesis, proposes that in a mature ecosystem, the singing of a species occupies a unique bandwidth in frequency and shifts in time to avoid competition, thus making the communication efficient. We hypothesize that selective pressure on communication complexity is required for differentiating and filling acoustic niches by a limited number of species, in addition to selective pressures on communication efficiency. To test this hypothesis, we built an evolutionary model where agents can emit complex sounds. Our simulations with the model demonstrate that selective pressure on communication efficiency and complexity leads to an evolution in spectral differentiation with a limited number of species filling the acoustic niche. This is the first demonstration of acoustic niche differentiation using an artificial life model with complex-sounding agents. We also propose multi-timescale complexity measurement, extending the Jensen–Shannon complexity using multi-scale permutation entropy. We analyze the evolved soundscape in the simulations using this measure. The result shows that multi-timescale complexity in soundscape evolved, suggesting that evolving niche differentiation leads to ecological complexity. We implement the extended model in real space and demonstrate that the system can adaptively generate sounds, differentiating acoustic niches with environmental sounds.

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.

Honeybees are highly social animals who live in large colonies, called hives. This study explores global patterns and dynamics observed in the beehive by tracking the individual behaviors. Previous research developed a high-throughput automatic monitoring system for honeybees ( Apis mellifera ) that tracked every individual bee in a hive, recording their positions, speed and orientations. This has been used to analyze the bees trophallaxis (two bees touching each other with their antennae to orally transferring liquid food (Free (1956))). network and calculate how often they communicate; it was found that the bee networks communicate in the intermittent manner in time, called bursts, much like human communication networks (Gernat et al. (2018)). Using this same dataset, we developed a new, complementary analysis that examined a different bee behavior that also follows a burst pattern: the bursts of kinetic energy that occur in beehives. Such bursts may be endogenous (i.e., spontaneous activity resulting from the internal interactions of bees) or exogenous (i.e., resulting from external perturbations). We sought to identify relationships between endogenous and exogenous bursts and the contributions of individual bees, as well as the relationship between the bees trophallaxis network and their kinetic bursting behaviors.