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Scope

The IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (TCDS) focuses on advances in the study of development and cognition in natural (humans, animals) and artificial (robots, agents) systems. It welcomes contributions from multiple related disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology. Articles on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are considered.

TCDS is co-sponsored by the Computational Intelligence Society, the Robotics and Automation Society, and the Consumer Electronics Society. TCDS is technically co-sponsored by the Computer Society.

Impact Score

CDS impact score
Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals.
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Featured Paper

Selected article from IEEE Transactions on Cognitive and Developmental Systems

Bootstrapping Q-Learning for Robotics From Neuro-Evolution Results 

Reinforcement learning (RL) problems are hard to solve in a robotics context as classical algorithms rely on discrete representations of actions and states, but in robotics both are continuous. It is proposed to define a process to make a robot build its own representation for an RL algorithm. The principle is to first use a direct policy search in the sensori-motor space, i.e., with no predefined discrete sets of states nor actions, and then extract from the corresponding learning traces discrete actions and identify the relevant dimensions of the state to estimate the value function. Once this is done, the robot can apply RL: 1) to be more robust to new domains and, if required and 2) to learn faster than a direct policy search. This approach allows to take the best of both worlds: first learning in a continuous space to avoid the need of a specific representation, but at a price of a long learning process and a poor generalization, and then learning with an adapted representation to be faster and more robust.

IEEE Transactions on Cognitive and Developmental Systems, Mar. 2018