This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants.

Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.

O presente estudo está vinculado a uma pesquisa que objetiva investigar o processo de construção da noção de algoritmo na atividade de programação realizada por crianças. Apresentamos, aqui, a análise de nove experimentos para a investigação de processos cognitivos envolvidos na atividade de movimentar um robô concreto [...]. Em uma primeira etapa, foram realizadas provas cognitivas, baseada no método clínico-piagetiano para a verificação do estádio do desenvolvimento intelectual de cada sujeito e uma segunda etapa onde os sujeitos resolveram problemas para fazer movimentar um objeto tangível. O experimento foi gravado em vídeo e analisado com auxílio do software de análise qualitativa MAXQDA2020®. Como conclusões preliminares, podemos indicar relações entre os estádios de desenvolvimento cognitivo infantil e o uso de diferentes estratégias de resolução de problemas envolvendo a construção de algoritmos para a programação de um robô. Palavras-chave: Desenvolvimento Cognitivo; Atividade de Programação; Epistemologia Genética; Robótica Educacional.