Automating the Design of Complex Systems

This chapter discusses the issue of whether it is possible to automate the design of rather complex workflows needed when addressing more complex data science tasks. The focus here is on symbolic approaches, which continue to be relevant. The chapter starts by discussing some more complex operators, including, for instance, conditional operators and operators used in iterative processing. Next, we discuss the issue of introduction of new concepts and the changes of granularity that can be achieved as a result. We review various approaches explored in the past, such as constructive induction, propositionalization, reformulation of rules, among others, but also draw attention to some new advances, such as feature construction in deep NNs. It is foreseeable that in the future both symbolic and subsymbolic approaches will coexist in systems exhibiting a kind of functional symbiosis. There are tasks that cannot be learned in one go, but rather require a sub-division into subtasks, a plan for learning the constituents, and joining the parts together. Some of these subtasks may be interdependent. Some tasks may require an iterative process in the process of learning. This chapter discusses various examples that can stimulate both further research and some practical solutions in this rather challenging area.