This study aims to conduct an experimental evaluation of personalized interventions to promote physical activity. Promoting health behaviors such as physical activity to improve well-being is an important social issue. In recent years, interventions tailored to individual needs (i.e., personalized interventions) have been required. Therefore, we used PLS-SEM to model the relationship between individually different personalities (i.e., personality traits) and the motivational ability of intervention for physical activity, with the goal of enabling personalized interventions. As a result of the experiment, the personalized intervention group based on the model showed a significantly higher implementation rate and continuation rate of physical activity compared to the non-personalized intervention group and the control group. These results suggest that the personalized intervention based on personality traits is effective in promoting physical activity.

Compressed video action recognition is an action recognition approach that can achieve efficient inference by directly classifying video data obtained from multiple features stored in compressed videos. Most conventional methods use multiple networks to process compressed video features. They explore the use of lightweight networks without affecting the classification performance to reduce the computational complexity of compressed video action recognition. This study explores another approach to reduce the computational complexity, by using a single network instead of multiple networks to process compressed video features. Although training a single network cannot yield the practical classification performance of conventional methods, we propose an extended MIMO training method for action recognition to simultaneously process different features. Our experiments demonstrate that our method is the most efficient in terms of computational complexity and can achieve classification performances comparable to conventional methods.

Artificial life is a research field devoted to the theoretical study of features of living systems, such as evolution and the brain. The field has developed philosophical concepts such as autopoiesis and emergence, alongside a large range of computational and experimental setups, from evolutionary simulations to robotics and chemical experiments.The complexity and diversity of the artificial life field is crucial to its community. Many researchers consider the community a real source of creativity and free-minded exchange of ideas on important questions. For ideas that don't fit neatly into a single “mainstream” field of science, there is value in examining and discussing them in a context free from departmental or disciplinary constraints, with the purpose of reaching a better knowledge of the fundamental mechanisms that govern living systems.Workers in the field of artificial life convene yearly at a conference, which most recently took place in 2018 in Tokyo. The ALIFE 2018 conference highlighted eight categories of topics:• Perception, cognition, behavior• Bio-inspired, cognitive, and evolutionary robotics; swarms• Ecological and social systems• Artificial chemistry, origins of life, computational biology• Complex dynamical systems and networks• Evolution of language, computational linguistics• Philosophy of mind, philosophy of science• Artificial-life–based art• Synthetic biology and wet artificial life• Education and society issuesIn this issue, we present a selection of recent studies on these topics, which were presented at ALIFE 2018 and which reflect the very rich heterogeneity of the field, and many of which are contributions from young scientists. These articles have been selected from a very diverse pool of submissions. The conference featured 112 papers, selected out of 212 submissions, of which 64 were accepted as oral presentations and 48 as poster presentations.We focused on top-rated, promising, and impactful studies by young contributors to artificial life research. The articles cover a set of concepts key to artificial life, and ground them in concrete models and outputs to better describe those concepts and make them actionable for the whole field. A simple classification of the current research paradigm in this volume can be described as follows: (i) autopoiesis, (ii) evolution and evolvability, (iii) emergence, and (iv) embodiment.The first three articles examined autopoiesis and embodiment. Randall Beer [1] investigated the initial-state dependence of the Game of Life to show how the initial distribution generates gliders but also annihilates and regenerates them as time passes. This is a prototypical life evolution example. This work is in the line of embodied autopoiesis, which is reconsidered by studying gliders and their peripheral configurations.Tamaru, Yui, and Hashida [9] demonstrated the self-organized motion of a pine cone, emphasizing its unexpected locomotion, which realizes a sensory-motor coupling. The motion of gliders and pine cones is different, but has that common property, resulting from environmental conditions (humidity for pine cones, and the bit configuration for gliders).Masumori et al. [5] discussed the concept of autopoiesis in terms of artificial and biological neural focused on networks. Their work proposes a new learning principle of neural networks called stimulus avoidance. If a network can avoid being stimulated from the outside, it does so by organizing its behavior to avoid the source of stimuli in the environment. If not, the network tends to inactivate its sensory part to prevent the information from the sensor neurons from spreading. So, effectively, the sensory inputs are removed.In contrast with Randy Beer's work [1], neural networks can self-organizingly change their boundary conditions to acquire robustness. A neural network protects itself from environmental stimuli, whereas gliders are totally fragile against external perturbation.The next articles examined aspects of evolutionary dynamics.Dolson et al. [2] proposed a set of metrics for analyzing evolutionary histories in digital evolution experiments, with the hope of boosting the computational evolution community's interest in using such measures to improve our understanding of evolutionary dynamics. The article offers some simple use cases, and points to code available online to compute the measures.Khajehabdollahi and Witkowski [3], by using restricted Boltzmann machines (RBMs) (installed agents), demonstrated that a critical state of an RBM is not always adaptive. The critical states should be selected through evolutionary dynamics for a particular task.Liard et al. [4] examined whether complexity is driven primarily by selection or a non-selective force, which is a central question in artificial life. Using the Aevol system for digital organisms, they designed a simple world and a simple genome, and applied directional selection pressure for a protein that can function to produce a triangle. They explored how an information metric for complexity at genomic and phenotypic levels appears to increase at low mutation rates, and as well, explored robustness and evolvability.Moore et al. [6] gave a tangible example of how artificial life models are a powerful tool to help society, in particular in the biomedical realm, to test hypotheses in great detail. Although methods such as genome-wide association study (GWAS) have allowed identifying thousands of genetic risk factors for hundreds of diseases, the genetic risk factors detected have very small effect sizes and tell little about the overall heritability of diseases. With the help of a heuristic simulation, the authors were able to ask questions about the complexity of genetic architectures, in particular whether common diseases are partly driven by non-additive interactions. The simulations were highly consistent with the magnitude and distribution of univariate effects seen in real data, as is corroborated by a comparison with large-scale studies of sporadic breast cancer.Veenstra et al. [10] studied how senescence, or intrinsic mortality, influences the evolvability of a population. They used the hierarchical if-and-only-if function as a deceptive fitness landscape, together with a spatial artificial life model. They showed an explicit relationship between mutation and mortality rate in a few different application scenarios. Their results support the premise that programmed death of individuals can have a beneficial effect on the evolvability of the entire population.The studies by Masumori et al. [5], Khajehabdollahi and Witkowski [3], and Beer [1] all addressed the important theme of embodiment, that is, how an embodied mind “as it could be” might develop a first-person perspective in a different but even more direct way. Tamaru, Yui, and Hashida [9] also addressed how intelligence can be embedded in a direct physical task, where an embodiment computes how to advance.The general theme of this conference was “Beyond AI.” Since around 2010, AI has rapidly become a major trend, and the use of big data has gradually invaded every area of science. AI does not need to visualize big data, nor does it have to solve any equations. Just from the big data itself, AI can extract patterns and forms to predict future outcomes. A missing piece of this new era driven by AI is embodiment and evolutionary perspectives, which are also the main foci of this volume.The fundamental principles of life are missing from current AI systems, which bear little of the richness of living systems. From the point of view of the ALife community, it is fairly evident that the post-AI era will need to invent something completely new, which can be done by leveraging research topics that are already active in the ALife community.If one is serious about designing systems that mimic human intelligence, the priority should be put on a theory of the nature of living (if not human) systems, on their emergence, and on their evolution through time. The understanding of such principles can then contribute to an effort to design intelligence, which could even come as an inevitable side effect of homeostasis and evolution in a complex and dynamic environment.Although this approach may seem like wishful thinking, possibly sprouting from a whole community's hallucination due to its dedication to tackling life as it could be, perhaps the rich interdisciplinarity of the field may manage to protect it from biases. Actually, looking at the major conferences about diverse types of machine learning, AI scientists themselves have generally started looking in the direction of ALife, in a search for creative ideas.AI research currently sees limitations, suggesting the arrival of a next AI winter, in which AI will pass on its popularity to other sectors of science. The first signs are the absence of new paradigms in machine learning, and the shift from the invention of new algorithmic principles to their use: We are now merely applying all the key inventions at our disposal, such as backpropagation, neural architectures, and powerful computation, to solve application problems in other fields. AI needs a next source of creativity, which will bring about the next step towards artificial general intelligence (AGI) or artificial superintelligence (ASI).In a sense, ALife has been pushing in the creative direction all along, in particular through its search for open-ended evolution (OEE), which is also the study of the production of novelty in nature. The extraordinary source of inventiveness and diversity seems to be key to the nature of living systems. In 2019, Artificial Life journal published two special issues (see [7] and [8] for overviews) that focused on OEE as an essential part of artificial systems as well as real living systems, from many different angles. Those issues and the current one could not be better aligned, for in order to capture the I in AI, one needs to focus on what is at its roots: the fundamental principles of life, including the juice that makes it maintain its creative productivity.ALife as a field has managed to develop a unique creative ecology for the study of living systems from first principles, by building them piece by piece from the most diverse substrates. Before this proves useful for enabling AI's next steps, ALife will probably undergo changes itself, following its two major conferences, which have now been merged into one since the Tokyo conference. Not only a central concept in this day and age, ALife also is a symbol of transdisciplinary, diverse, and open-ended scientific endeavor, which exemplifies the creativity required to make significant leaps in understanding our universe, and us as cognitive beings living, evolving, and growing in it.

Recently, the use of reinforcement-learning algorithms has been proposed to create value and policy functions, and their effectiveness has been demonstrated using Go, Chess, and Shogi. In previous studies, the policy function was trained to predict the search probabilities of each move output by Monte Carlo tree search; thus, a number of simulations were required to obtain the search probabilities. We propose a reinforcement-learning algorithm with game of self-play to create value and policy functions such that the policy function is trained directly from the game results without the search probabilities. In this study, we use Hex, a board game developed by Piet Hein, to evaluate the proposed method. We demonstrate the effectiveness of the proposed learning algorithm in terms of the policy function accuracy, and play a tournament with the proposed computer Hex algorithm DeepEZO and 2017 world-champion programs. The tournament results demonstrate that DeepEZO outperforms all programs. DeepEZO achieved a winning percentage of 79.3% against the world-champion program MoHex2.0 under the same search conditions on 13 × 13 board. We also show that the highly accurate policy functions can be created by training the policy functions to increase the number of moves to be searched in the loser position.

This paper investigates time-delay effects of the human social interaction to understand how human can adapt to the time delay, which will be required in software agents to establish a harmonic interaction with human. We performed the minimal experiments of social interaction called perceptual crossing experiments with time delay. Our result shows that the social interaction breaks down when the total amount of time delay is given more than about one second. However, the interaction breaks more easily when the time delay is given to both participants than to either participant.

Our aim of this study is to investigate the possibility of estimating interruptibility from Electroencephalograms (EEG). We focus on contingent negative variation which is elicited in response to concentration. Our results support that our method could correctly detects the user's internal states such as just watching or actively remembering even the external situations were completely same and also CNV is elicited when controlling continuous motion.

When training for dance or sports, amateurs observe and mimic the physical exertion of experts to acquire skill by observing others. However, humans don't memorize consecutive operations, but instead make discrete divisions and memorize body movements. The posture that becomes a key from consecutive operations is removed and memorized for discretion in a process we call segmentation. The movement is reproduced by smooth interpolations between the clause and the clause memorized as segmentation. In the first experiment, we clarified the existence of this segmentation by showing video of smooth movements to participants who selected the segment/frame that became a key. We confirmed commonness in this segment selection. In experiment 2, to reflect the physical exertion that made the segmentation, the participants input force to force sensors by a control stick. We showed video of smooth movements to them and simultaneously input the force pattern that corresponds to the movement. The force pattern, which uniformly converges by such repetition, is different depending on each participant. We call this method the “extraction of segmented behavioral intention.” On the other hand, we can arbitrarily change the speed of the physical exertion by expanding and contracting the segments of the movement video that obtained the force pattern by “extraction of segmented behavioral intention.” Since the expanded and contracted pattern range at a certain speed even when the movement speed changed, we preserved the shape and the selected segments. However, the segment shape changes when the movement speed is constantly fast.

This study presents an experiment that integrates a semiotic investigation with a dynamical perspective on embodied social interactions. The primary objective is to study co-creativity of a communication system on the human-human nonverbal interaction. Throughout the experiment, the results of established communication system on uni- and bi-directional interactions are compared. Finally, the role of co-creativity in the evolution of communication systems will be discussed.

We developed a new pseudo-attraction force display using four vibrating motors. The advantage of this device is that it uses phase difference control to alter the time width of pulses to generate asymmetric oscillation. It does not require altering the frequency of the vibrating motors. In this study, we investigated the influence of the pulse width generated in the device using vibrating motors on the perceptive intensity of pseudo-attraction force. From the result, the oscillation having shorter pulse widths within the range of 10-20 ms induced the “being-pulled” sensation more strongly.

Our aim of this paper is to investigate the human communication in terms of two following questions. One is how human can know the fact that an interacting partner is human. Another is how non-communicative behaviour can become communicative. To answer these questions, we performed two experiments exploiting the idea of perceptual crossing experiments. As a result, we will show that the turn-taking structure supports humanlikeness and human communication in the primitive non-verbal interaction. Our results will be discussed with ambient interface technology.

When multicasting through wireless multihop networks, frame losses at the wireless link cause data losses at the application layer because of no ACK (Acknowledgment) at both data link layer and transport layer. The authors propose a novel multicast scheme to reduce data losses at the application layer and to improve reliability. The scheme incorporates a retransmission mechanism into the data link layer and selects the reliable IP-based unicast paths. The verification of the proposed multicast scheme is performed with wireless LAN interface equipped nodes in an actual office environment, where an NTSC-Quality MPEG4 movie was successfully forwarded to a 5-hop destination node.

When two distant persons establish cooperative interaction, how to share their motions and sensations and how to adjust or revise their motions are important for cooperation. In this paper, we propose an approach for sharing first-person perspectives to establish collaboration and to transmit a skill from one to another. We developed a view-sharing system to realize such interaction. To investigate the fundamental behavioral property of humans with our system, we examined a simple collaborated behavior and found that a situation where subjects only see their partner's view improves velocity following. By exploiting this property, we transmitted the basic skill required for the Diabolo trick to non-skilled persons. Through our system, the performances of non-skilled persons with the assistance of a skilled person surpassed individual performances.

Turn-taking behavior is simulated in a coupled-agents system. Each agent is modeled as a mobile robot with two wheels. A recurrent neural network is used to produce the motor outputs and to hold the internal dynamics. Agents are developed to take turns on a two-dimensional arena by causing the network structures to evolve. Turn taking is established using either regular or chaotic behavior of the agents. It is found that chaotic turn takers are more sensitive in response to inputs from the other agent. Conversely, regular turn takers are comparatively robust against noisy inputs, owing to their restricted dynamics. From many observations, including turn taking with virtual agents, we claim that there is a complementary relationship between robustness and adaptability. Furthermore, by investigating the recoupling of agents from different GA generations, we report the emergence of a new turn-taking behavior. Potential for synthesizing a new form of interaction is another characteristic of chaotic turn takers.

To study the interfacial complexity between an agent and its environment such as the adaptive selection aspects of sensory inputs, we propose a new coupling mechanism, called autonomous coupling, where an agent can spontaneously switch on or off its interaction with the environment.An oscillatory neural system with autonomous coupling sums the sensory inputs and initiates action selection via a sensorimotor coupling.An example task we designed to show dynamical categorization is the classification of light frequencies.An evolved agent selects specified light frequencies by approaching them and avoiding light of other frequencies.Dynamical categorization and active coupling are the key concepts for the understanding of situated and embodied cognitive functions.