Current mainstream AI, at least as presented in media and measured by the number of people involved and papers published, is mainly about big data, deep learning, and recently trendy large language models. All these are techniques that are data-driven, model-free, and number-crunching. Their immense success in some areas, such as computer vision and natural language processing, started the next hype in the era of AI, which brings a question whether neural approaches, after being dismissed at the beginning of the AI era, finally conquered the world of AI and proved applicable to every problem. A deeper look at these new techniques shows they have similar issues as the old-fashioned AI techniques in the past: brittleness, making strange mistakes, and being highly dependent on data used for training. Moreover, there are problems with the explainability of results and no guarantees provided, which is a crucial issue in some application areas.

In this paper, we look at core principles of the neural ML techniques, that is, being data-driven rather than knowledge-based and being model-free rather than model-based, and we argue that symbolic knowledge models can still contribute to the design of trustworthy and explainable AI systems. Specifically, we focus on hierarchical reasoning, namely hierarchical planning, which is useful for highly complex problems but is not addressed by current neural models. We propose a research plan consisting of solving specific problems in hierarchical planning as an example of a knowledge-intensive approach to problem-solving. We show close connections between these problems that allow a smooth transition between solving techniques used to solve these problems. We also propose an ultimate goal of this endeavor, that is, autonomous construction of hierarchical planning models, that addresses the crucial problem of knowledge-based approaches -- how to obtain a formal model (extract knowledge from data).