Abstract

AbstractThis 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.

Highlights

  • The aim of this chapter is to discuss the issue of automating the design of more complex systems than the ones discussed in previous chapters

  • Many systems exist that interact with the exterior, to the best of our knowledge not much work has been done up to now in the area that would combine the introduction of new concepts from the exterior with their introduction relying on autonomous methods

  • Michalski is known for introducing the term constructive induction

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Summary

15 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. We review various approaches explored in the past, such as constructive induction, propositionalization, reformulation of rules, among others, and 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. This chapter discusses various examples that can stimulate both further research and some practical solutions in this rather challenging area

15.1 Introduction
15.2 Exploiting a Richer Set of Operators
15.3 Changing the Granularity by Introducing New Concepts
15.3.1 Defining new concepts by clustering
15.3.2 Constructive induction
15.3.3 Reformulation of theories consisting of rules
15.3.4 Introduction of new concepts expressed as rules
15.3.5 Propositionalisation
15.3.6 Automatic feature construction in deep NNs
15.3.7 Reusing new concepts to redefine ontologies
15.4 Reusing New Concepts in Further Learning
15.5 Iterative Learning
15.5 Iterative Learning 291
Findings
15.6 Learning to Solve Interdependent Tasks
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