Abstract—Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without

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.

What is the enactive approach to cognition? Over the last 15 years this banner has grown to become a respectable alternative to traditional frameworks in cognitive science. It is at the same time a label with different interpretations and upon which different doubts have been cast. This paper elaborates on the core ideas that define the enactive approach and their implications: autonomy, sensemaking, emergence, embodiment, and experience. These are coherent, radical and very powerful concepts that establish clear methodological guidelines for research. The paper also looks at the problems that arise from taking these ideas seriously. The enactive approach has plenty of room for elaboration in many different areas and many challenges to respond to. In particular, we concentrate on the problems surrounding several theories of value-appraisal and valuegeneration. The enactive view takes the task of understanding meaning and value very seriously and elaborates a proper scientific alternative to reductionist attempts to tackle these issues by functional localization. Another area where the enactive framework can make a significant contribution is social interaction and

Embodied systems actively structure information sampled by their sensors as they engage in sensorimotor interactions with their environment. Can information structure serve as an evolutionary principle that shapes behavior and leads to increased coordination? Here we address this question by attempting to evolve coordinated behavior in a simulated creature subjected to behavioral and information-theoretical cost functions. Our results show that maximizing information structure is highly effective in generating coordinated behavior, providing further support for a potential central role of actively generated information structure in embodied cognition.

Maturana and Varela's notion of autopoiesis has the potential to transform the conceptual foundation of biology, as well as the cognitive, behavioral and brain sciences. In order to fully realize this potential, however, the concept of autopoiesis and its many consequences require significant further theoretical and empirical development. A crucial step in this direction is the formulation and analysis of models of autopoietic systems. This paper sketches the beginnings of such a project by examining a glider from the Game of Life in autopoietic terms. Such analyses can clarify some of the key ideas underlying autopoiesis and draw attention to some of the central open issues. This paper also examines the relationship between an autopoietic perspective on cognition and recent work on dynamical approaches to the behavior and cognition of situated, embodied agents.

Abstract Turn-taking behavior is simulated in a coupled-agents system. Each agent is modeled as a mobile robot with two wheels. A recurrent neural network is used to produce the motor outputs and to hold the internal dynamics. Agents are developed to take turns on a two-dimensional arena by causing the network structures to evolve. Turn taking is established using either regular or chaotic behavior of the agents. It is found that chaotic turn takers are more sensitive in response to inputs from the other agent. Conversely, regular turn takers are comparatively robust against noisy inputs, owing to their restricted dynamics. From many observations, including turn taking with virtual agents, we claim that there is a complementary relationship between robustness and adaptability. Furthermore, by investigating the recoupling of agents from different GA generations, we

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In this paper we describe the implications from a general theory of category formation of a set of experiments in which embodied artificial agents are evolved for the ability to accomplish simple tasks. In particular we will focus on how categories might emerge from the dynamical interaction between an agent and its environment and on the relation between categories and behavior. Finally, we will introduce and discuss the notion of action-mediated categories, that is the notion of internal states that provide indirect and implicit information about the external environment and/or the agent/environment relation by exploiting the effects resulting from a stereotypic way of interacting with the environment. 1.

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This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.