One of the challenging aspects of open ended or lifelong agent development is that the final behaviour for which an agent is trained at a given moment can be an element for the future creation of one, or even several, behaviours of greater complexity, whose purpose cannot be anticipated. In this paper, we present modular influence network design (MIND), an artificial agent control architecture suited to open ended and cumulative learning. The MIND architecture encapsulates sub behaviours into modules and combines them into a hierarchy reflecting the modular and hierarchical nature of complex tasks. Compared to similar research, the main original aspect of MIND is the multi layered hierarchy using a generic control signal, the influence, to obtain an efficient global behaviour. This article shows the ability of MIND to learn a curriculum of independent didactic tasks of increasing complexity covering different aspects of a desired behaviour. In so doing we demonstrate the contributions of MIND to open-ended development: encapsulation into modules allows for the preservation and re-usability of all the skills acquired during the curriculum and their focused retraining, the modular structure serves the evolving topology by easing the coordination of new sensors, actuators and heterogeneous learning structures.

开放式或终身代理开发的挑战性方面之一是，在给定的时间对代理进行培训的最终行为可能是将来创建一个甚至多个复杂性更高的行为的要素，而这些行为的目的是被预料到。在本文中，我们提出了模块化影响力网络设计（MIND），这是一种适用于开放式和累积式学习的人工代理控制体系结构。MIND体系结构将子行为封装到模块中，并将其组合为一个层次结构，以反映复杂任务的模块化和层次结构性质。与类似的研究相比，MIND的主要原始方面是使用通用控制信号（影响）的多层层次结构，以获取有效的全局行为。本文显示了MIND学习独立的教学任务课程的能力，这些课程的难度越来越大，涵盖了所需行为的各个方面。通过这样做，我们证明了MIND对开放式开发的贡献：将模块封装到模块中可以保留和重用课程中所获得的所有技能并进行有针对性的再培训，而模块化结构则通过简化协调来服务于不断发展的拓扑新的传感器，执行器和异构学习结构。