ANN-based Representation Learning in a Lifelong Open-ended Learning Cognitive Architecture

摘要

The frontier in robot autonomy is currently Lifelong Open-Ended Autonomy (LOLA). Within these settings, a robot must be able to operate and learn in domains that are unknown at design time as well as reuse knowledge learnt in one domain to facilitate learning in others throughout its lifetime. Achieving LOLA goes beyond learning specific algorithms and puts us squarely in the realm of cognitive architectures; however, most cognitive architectures were not built to address the LOLA problem, and thus, lack components and capabilities that would be required for it. In fact, even though there is a growing literature on learning representations, especially in the framework of reinforcement and deep learning, hardly any cognitive architecture considers the issue of autonomously learning representations, which is a crucial problem to be able to efficiently learn and abstract information when seeking LOLA. This paper provides a vision of the general requirements in terms of learning knowledge representations within cognitive architectures geared towards LOLA and addresses a specific problem in this context: the problem of learning representations that facilitate obtaining world and utility models and deciding on actions in situations where multiple goals can be activated. The work is carried out in the framework of the development of the e-MDB cognitive architecture for real robots operating autonomously in real domains.