The ability to automatically learn movements and behaviors of increasing complexity is a long-term goal in autonomous systems. Indeed, this is a very complex problem that involves understanding how knowledge is acquired and reused by humans as well as proposing mechanisms that allow artificial agents to reuse previous knowledge. Inspired by Jean Piaget's theory's first three sensorimotor substages, this work presents a cognitive agent based on CONAIM (Conscious Attention-Based Integrated Model) that can learn procedures incrementally. Throughout the paper, we show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Experiments were conducted with a humanoid robot in a simulated environment modeled with the Cognitive Systems Toolkit (CST) performing an object tracking task. The system is modeled using a single procedural learning mechanism based on Reinforcement Learning. The increasing agent's cognitive complexity is managed by adding new terms to the reward function for each learning phase. Results show that this approach is capable of solving complex tasks incrementally.

翻译：自动学习日益复杂的动作与行为的能力，是自主系统领域的长期目标。这确实是一个极其复杂的问题，涉及理解人类如何获取并复用知识，同时提出使人工体能够复用先前知识的机制。受让·皮亚杰理论中前三个感知运动子阶段的启发，本研究提出了一种基于CONAIM（基于注意力与意识的集成模型）的认知体，能够增量式地学习程序。全文展示了每个子阶段所需的认知功能，以及添加新功能如何帮助解决智能体先前无法完成的任务。在利用认知系统工具包（CST）建模的模拟环境中，使用人形机器人进行了物体跟踪任务实验。系统采用基于强化学习的单一程序学习机制建模。通过为每个学习阶段向奖励函数添加新项，管理智能体认知复杂度的增长。结果表明，该方法能够增量式地解决复杂任务。