Several studies that deal with the acquisition of concepts in a bottom-up manner from experiences in the physical space exist, but there are few of them that deal with the bidirectional interaction between symbolic operations and experiences in the physical world. It was shown that a shared module neural network succeeded in generating a bottom-up spatial representation of the external world, without involving learning of the signals of the spatial structure. Furthermore, the module can understand the external map as a symbol based on its spatial representation, and top-down navigation can be performed using the map. In this study, we extended this model and proposed a simulation model that unifies the emergence of a number representation, learning of symbol manipulation on the representation, and top-down understanding of symbol manipulation onto the physical world. Our results show that the learning results of the symbol manipulation can be applied to the physical world prediction, and our proposed model succeeded in grounding symbol manipulation onto physical experiences.

This study reveals what kind of temporal and spatial patterns form when learning in an adversarial relationship between two individuals. The model was implemented by coupling generative adversarial networks, which are well-known in the field of machine learning. The obtained temporal patterns resulted in chaos with a positive Lyapunov exponent for time-series learning, whereas spatial pattern learning produced structured patterns with a higher fractal dimension, not just more complexity with a higher entropy.

Chemotaxis is a phenomenon whereby organisms like ameba direct their movements responding to their environmental gradients, often called gradient climbing. It is considered to be the origin of self-movement that characterizes life forms. In this work, we have simulated the gradient climbing behaviour on Neural Cellular Automata (NCA) that has recently been proposed as a model to simulate morphogenesis. NCA is a cellular automata model using deep networks for its learnable update rule and it generates a target cell pattern from a single cell through local interactions among cells. Our model, Gradient Climbing Neural Cellular Automata (GCNCA), has an additional feature that enables itself to move a generated pattern by responding to a gradient injected into its cell states.

Swarms of birds and fish produce well-organized behaviors even though each individual only interacts with their neighbors. Previous studies attempted to derive individual interaction rules using heuristic assumptions from data on captured animals. We propose a machine learning method to obtain the sensorimotor mapping mechanism of individuals directly from captured data. Data on swarm behaviors in fish was captured, and individual positions are determined. The sensory inputs and motor outputs are estimated and used as training data. A simple feedforward neural network is trained to learn the sensorimotor mapping of individuals. The trained network is implemented in the simulated environment and resulting swarm behaviors are investigated. As a result, our trained neural network could reproduce the swarm behavior better than the Boids model. The reproduced swarm behaviors are evaluated in terms of three different measures, and the difference from the Boids model is discussed.

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)

Animals develop spatial recognition through visuomotor integrated experiences. In nature, animals change their behavior during development and develop spatial recognition. The developmental process of spatial recognition has been previously studied. However, it is unclear how behavior during development affects the development of spatial recognition. To investigate the effect of movement pattern (behavior) on spatial recognition, we simulated the development of spatial recognition using controlled behaviors. Hierarchical recurrent neural networks (HRNNs) with multiple time scales were trained to predict visuomotor sequences of a simulated mobile agent. The spatial recognition developed with HRNNs was compared for various values of randomness of the agent’s movement. The experimental results show that spatial recognition was not developed for movements with a randomness that was too small or too large but for movements with intermediate randomness.

Bird song is one of the phenomena that increase in complexity through evolution. A complex song is known to be advantageous for survivability and birds are known to learn how to sing a song from each other. From these facts, we have a hypothesis that adversarial imitation learning plays a major role in the evolution process of a complex song. There is a previous study that demonstrates the complexation of a bird song time series by modeling the process of adversarial imitation learning using a logistic map. However, the real bird songs have much variety and time dependencies, like grammar. Therefore, in this study, adversarial imitation learning is modeled using an artificial neural network that can approximate any function. The network learns adversarial imitation using the gradient descent method. By making such changes, the results of our study show that the generated bird songs evolve through the process of adversarial imitation learning to chaos, as seen in the previous models.

Spatial recognition is the ability to recognize the environment and generate goal-directed behaviors, such as navigation. Animals develop spatial recognition by integrating their subjective visual and motion experiences. We propose a model that consists of hierarchical recurrent neural networks with multiple time scales, fast, medium, and slow, that shows how spatial recognition can be obtained from only visual and motion experiences. For high-dimensional visual sequences, a convolutional neural network (CNN) was used to recognize and generate vision. Our model, which was applied to a simulated mobile agent, was trained to predict future visual and motion experiences and generate goal-directed sequences toward destinations that were indicated by photographs. Due to the training, our model was able to achieve spatial recognition, predict future experiences, and generate goal-directed sequences by integrating subjective visual and motion experiences. An internal state analysis showed that the internal states of slow recurrent neural networks were self-organized by the agent’s position. Furthermore, such representation of the internal states was obtained efficiently as the representation was independent of the prediction and generation processes.