The goal of this work is to explore the feasibility of symbolic (S) transfer learning (TL). The main activities are:
- design a STL algorithm for tabular data;
- implementation of the algorithm;
- perform experiments on real world datasets.
Similarly to the well known TL, the main goal is to transfer information about a domain from a source towards a target (usually they both are ML models). The key difference from TL is that STL does not transfer subsymbolic information – such as layers of a DNN – but symbolic information (e.g., logic predicates). The advantages of relying on symbolic formalisms are manifolds: 1. the information is both human and machine interpretable, 2. the information is concise thanks to intensional representation, 3. the method is target agnostic, i.e., it does not make assumption on the undergoing ML model that will receive the knowledge.
Some materials:
- STL idea
- STL in MAS
