Towards a vygotskyan cognitive robotics: the role of language as a cognitive tool

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of \u27embodied cognition\u27 with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan Cognitive Robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life

Evolution of cooperation in artificial ants

The evolution of cooperation is a fundamental and enduring puzzle in biology and the social sciences. Hundreds of theoretical models have been proposed, but empirical research has been hindered by the generation time of social organisms and by the difficulties of quantifying costs and benefits of cooperation. The significant increase in computational power in the last decade has made artificial evolution of simple social robots a promising alternative. This thesis is concerned with the artificial evolution of groups of cooperating robots. It argues that artificial evolution of robotic agents is a powerful tool to address open questions in evolutionary biology, and shows how insights gained from the study of artificial and biological multi-agent systems can be mutually beneficial for both biology and robotics. The work presented in this thesis contributes to biology by showing how artificial evolution can be used to quantify key factors in the evolution of cooperation in biological systems and by providing an empirical test of a central part of biological theory. In addition, it reveals the importance of the genetic architecture for the evolution of efficient cooperation in groups of organisms. The work also contributes to robotics by identifying three different classes of multi-robot tasks depending on the amount of cooperation required between team members and by suggesting guidelines for the evolution of efficient robot teams. Furthermore it shows how simulations can be used to successfully evolve controllers for physical robot teams

Evolution of division of labor in artificial societies

Natural and artificial societies often divide the workload between specialized members. For example, an ant worker may preferentially perform one of many tasks such as brood rearing, foraging and nest maintenance. A robot from a rescue team may specialize in search, obstacle removal, or transportation. Such division of labor is considered crucial for efficient operation of multi-agent systems and has been studied from two perspectives. First, scientists address the "how" question seeking for mechanical explanations of division of labor. The focus has been put on behavioral and environmental factors and on task allocation algorithms leading to specialization. Second, scientists address the "why" question uncovering the origins of division of labor. The focus has been put on evolutionary pressures and optimization procedures giving rise to specialization. Studies have usually addressed one of these two questions in isolation, but for a full understanding of division of labor the explanation of the origins of specific mechanisms is necessary. Here, we rise to this challenge and study three major transitions related to division of labor. By means of theoretical analyses and evolutionary simulations, we construct a pathway from the occurrence of cooperation, through fixed castes, up to dynamic task allocation. First, we study conditions favoring the evolution of cooperation, as it opens the doors for the potentially following specialization. We demonstrate that these conditions are sensitive to the mechanisms of intra-specific selection (or "selection methods"). Next, we take an engineering perspective and we study division of labor at the genetic level in teams of artificial agents. We devise efficient algorithms to evolve fixed assignments of agents to castes (or "team compositions"). To this end, we propose a novel technique that exchanges agents between teams, which greatly eases the search for the optimal composition. Finally, we take a biological perspective and we study division of labor at the behavioral level in simulated ant colonies. We quantify the efficiency of task allocation algorithms, which have been used to explain specialization in social insects. We show that these algorithms fail to induce precise reallocation of the workforce in response to changes in the environment. We overcome this issue by modeling task allocation with artificial neural networks, which lead to near optimal colony performance. Overall, this work contributes both to biology and to engineering. We shed light on the evolution of cooperation and division of labor in social insects, and we show how to efficiently optimize teams of artificial agents. We resolve the encountered methodological issues and demonstrate the power of evolutionary simulations to address biological questions and to tackle engineering problems