"Go to the Children": Rethinking to Intelligent Agent Design and Programming in a Developmental Learning Perspective

Abstract

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p1809.pdf **Abstract:** In agent programming and engineering, learning has been explored so far mainly as an effective AI technique to tackle specific problems, improving agents' (and MAS as well) adaptability and autonomy at runtime. In this paper we consider a broader role of learning, impacting on the full spectrum of agent programming and engineering - from design to runtime. In particular, we introduce a perspective in which agent development is conceived as a kind developmental learning process of the agent itself. In that view, a software agent "is born" with some core domain-independent learning capabilities at the architectural level and is grown up in a proper learning environment to acquire the proper skills to be able then to achieve its designed objectives once it will be deployed, analogously to children in the human case. The idea relies on - on the one hand - existing literature about how learning has been injected in cognitive architectures as developed in cognitive science (e.g. SOAR, LIDA), as well as in agent architecture such as BDI. On the other hand, the idea drawn inspiration from human science, in particular the literature investigating the role of learning in human development, children in particular, and the impact of capabilities on human autonomy and practical reasoning. From a software engineering point of view, this idea is meant to be a conceptual baseline to explore general-purpose architectures and methodologies - eventually extending existing ones, such as the BDI - to seamlessly and systematically integrate both parts engineered by developers (e.g. plans) and parts learnt by the agent, eventually adopting different learning strategies. Main challenges of this view include devising the model and features for the primitive domain-independent learning system to be injected in agent core architecture and reasoning cycle, to support both developmental/continual learning, and how to structure the learning process occurring in the learning environment during the design/development stage. In the paper, causal reasoning and learning are identified and discussed as a main reference to this purpose, to be integrated with practical reasoning as implemented by modern agent architectures and technologies.