Social search (using social information to locate resources) has evolved stably across a wide range of species. The current research systematically investigates dynamical interactions between social search strategies of consumers and clustering of resource environments in simple 2D worlds. Previous work finds that clustering of resources (e.g., information, food) promotes use of social search, and other studies find the corresponding effect that social search leads to increased resource clustering. In Experiment 1 and 2 we replicate these results in simulations by fixing resource distributions and social search respectively at different levels and observing their influence on the other. Our results additionally show an inverse U-shaped trend between the two—as resource clustering increases, so does social search (as expected); however, at very high values of clustering, adaptive benefits of social search decline. Similar trends are obtained when social search is manipulated and resulting resource clustering is analyzed. In Experiment 3, we simulate dynamical systems where both social search and resource clustering are left unconstrained so they can mutually influence one other. These simulations are representative of real-world systems where species can flexibly alter their search strategies in response to the environment (e.g., through learning or evolution) and resource distributions are in turn influenced by consumer behavior. Here, we find that both social search and resource clustering evolve to positive values, indicating that they may be stable states of such systems. Our results have implications across a wide range of search domains—similar dynamics between social search and resource clustering are observed in multidimensional environments of informational and cultural search and simpler 2- and 3-D environments of ecological search.

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.

This paper aims at considering novel and practical applications of ALife techniques to design a co-creative social dynamics in an online and virtual space, which is becoming important because of the recent emergence of various types of online communication platforms due to outbreaks of COVID-19. Recently, spatial and online communication services, such as SpatialChat, have attracted more attention. Each participant is represented as an avatar or icon in a virtual 2D space. She can move it around in the space and listen to neighbors’ voices of which volume become louder as they get closer to her. However, the overall structure of communications tends to be deadlocked, which might make participants lose chances to communicate with many other people. We design and investigate a virtual agent, called “facilitator agent,” as a study towards realization of practical agents that facilitate novel and cooperative interactions in a spatial and online communication by giving human participants opportunities to communicate with many others cooperatively. We adopt a Social Particle Swarm (SPS) model to simulate group dynamics in this type of communication service. We assume several behavioral patterns of a facilitator agent with fixed game-theoretical strategies and several movement strategies. We discuss how incorporating a single facilitator agent into the space can increase novel and cooperative interactions in several behavioral settings of the facilitator agent. We also report on a preliminary experiment on designing a facilitator agent using a deep reinforcement learning technique.

This programmatic paper continues a series of works that we are dedicating to introduce a novel research program in AI, which we call Autopoietic SB-AI to indicate two basic elements of its procedural architecture. (1) The first element is the innovative methodological option of synthetically studying the cognitive domain based on the construction and experimental exploration of wetware –i.e., chemical – models of cognitive processes, using techniques defined in the field of Synthetic Biology (SB). (2) The second element is the theoretical option of developing SB models of cognitive processes based on the theory of autopoiesis. In our previous works we focused on the epistemological and theoretical groundings of Autopoietic SB-AI. In this contribution, after a general presentation of this research program, we introduce the SB technical framework that we are developing to orient Autopoietic SB-AI towards a twofold goal: building organizationally relevant wetware models of minimal biological-like systems (i.e., synthetic cells), and, on this basis, contributing to the scientific exploration of minimal cognition.

We focus on the connection between internal processes of regulation and their influence in the formation of behavioral patterns, and how they serve to provide a basis from where a biological organism derives a relation of value with its own activity. We touch on ongoing work exploring simulation modelling approaches to try to understand more about the issues required to overcome in order to capture the circularity involved in these processes, with particular attention to learning and sequential decision making formalisms of the perception-action loop, such as reinforcement learning and active inference. We describe an experimental setup where an agent learns to act in order to maintain the set of constraints that emerge and unfold as a consequence of the particular niche it inhabits.

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.