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 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 introduce the Concurrent Modular Agent (CMA), a framework that orchestrates multiple Large-Language-Model (LLM)-based modules that operate fully asynchronously yet maintain a coherent and fault-tolerant behavioral loop. This framework addresses long-standing difficulties in agent architectures by letting intention emerge from language-mediated interactions among autonomous processes. This approach enables flexible, adaptive, and context-dependent behavior through the combination of concurrently executed modules that offload reasoning to an LLM, inter-module communication, and a single shared global state.We consider this approach to be a practical realization of Minsky's Society of Mind theory. We demonstrate the viability of our system through two practical use-case studies. The emergent properties observed in our system suggest that complex cognitive phenomena like self-awareness may indeed arise from the organized interaction of simpler processes, supporting Minsky-Society of Mind concept and opening new avenues for artificial intelligence research. The source code for our work is available at: https://github.com/AlternativeMachine/concurrent-modular-agent.

This study simulates the dynamics of a collection of clonal agents responding to chemical gradients (chemotaxis) to demonstrate the evolution of individual variation. To build our multi-agent simulation, we first optimized single agents that rely on a neural network to perform chemotaxis. We then constructed multi-agent simulations using clones of these evolved individuals. We find that mutual interactions lead to the emergence of behavioral variation. We also find population-level performance degradation during later evolutionary stages, despite maintained high individual performance and simplified neural architectures. This decline occurred because agents developed reduced sensory-motor coupling. This latter finding demonstrates that incentives for individual variation worked against the collective interest.

Flocking behavior is a good example of viewing life as an emergent phenomenon. Flocking behavior can be studied using the boids model and by simulating a large scale model we can define and study how superorganisms can emerge in the model. A superorganismic is created by the emergence of new interaction among large flocks. In accordance with this definition, we found that velocity range is a critical parameter driving the behaviors as superorganisms, that is, we observed the emergence of superorganism dynamics by varying the velocity range. The agent’s velocity distribution and the Vicsek’s order parameter is used to quantify the transition to superorganism status.