Emergence of communication in embodied agents: Co-adapting communicative and non-communicative behaviours

In this paper I discuss in which conditions a population of embodied and situated agents that have to solve problems that requires cooperation might develop forms of ritualized interaction and communication. After reviewing the most relevant literature I will try to identify the the main open research problems and the most promising research directions. More specifially I will discuss: (a) the type of problems, the agents’ characteristics, and the environmental/social conditions that might facilitate the emergence of an ability to interact and communicate, and (b) the behavioral and cognitive capabilities that are crucial for the development of forms of communication of different complexity. 1

Cell division and migration in a 'genotype' for neural networks

Much research has been dedicated recently to applying genetic algorithms to populations of neural networks. However, while in real organisms the inherited genotype maps in complex ways into the resulting phenotype, in most of this research the development process that creates the individual phenotype is ignored. In this paper we present a model of neural development which includes cell division and cell migration in addition to axonal growth and branching. This reflects, in a very simplified way, what happens in the ontogeny of real organisms. The development process of our artificial organisms shows successive phases of functional differentiation and specialization. In addition, we find that mutations that affect different phases of development have very different evolutionary consequences. A single change in the early stages of cell division/migration can have huge effects on the phenotype while changes in later stages have usually a less drammatic impact. Sometimes changes that affect the first developental stages may be retained producing sudden changes in evolutionary history

On the Unexpected Abilities of Large Language Models

Large Language Models (LLMs) are capable of displaying a wide range of abilities that are not directly connected with the task for which they are trained: predicting the next words of human-written texts. In this article, I review recent research investigating the cognitive abilities developed by LLMs and their relation to human cognition. I discuss the nature of the indirect process that leads to the acquisition of these cognitive abilities, their relation to other indirect processes, and the implications for the acquisition of integrated abilities. Moreover, I propose the factors that enable the development of abilities that are related only very indirectly to the proximal objective of the training task. Finally, I discuss whether the full set of capabilities that LLMs could possibly develop is predictable.Comment: 13 page

Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm

In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot’s body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules

Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents

As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module dedicated to feature extraction trained through self-supervised methods. In this paper we report additional experiments supporting this hypothesis and we demonstrate how the advantage provided by feature extraction is not limited to problems that benefit from dimensionality reduction or that involve agents operating on the basis of allocentric perception. We introduce a method that permits to continue the training of the feature-extraction module during the training of the policy network and that increases the efficacy of feature extraction. Finally, we compare alternative feature-extracting methods and we show that sequence-to-sequence learning yields better results than the methods considered in previous studies

Duplication of modules facilitates the evolution of functional specialization

The evolution of simulated robots with three different architectures is studied. We compared a non-modular feed forward network, a hardwired modular and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state

Elman neural networks and time integration for object recognition

We consider a system based on an Elman network for a categorization task. Four objects are investigated by an automa walking around in circles. The shapes are derived from four version of a cross: square, thick cross, critical cross and thin cross. Therefore, the input of the system is represented by the distance-wave relieved by the sensor at each step. We let several parameters vary: starting point and speed of the automa walk, radius of the circle and size of the shape. The system is trained using a back-propagation algorithm. We describe the complete setup of the parameters and noises, which the automa will have to face for the prediction/categorization task