I'm coordinating the research project New Computational Paradigms for Explanatory Modeling of Complex Systems (Nowe Paradygmaty Obliczeniowe dla Konstrukcji Wyjaśniających Modeli Systemów Złożonych), granted by National Science Center, DEC-2011/01/B/ST6/07318, 8.12.2011-7.12.2014.
Keywords: complex systems, modeling, evolutionary computation, genetic programming, machine learning, feature selection and construction.
This project is intended to combine in a novel way selected concepts from branches of artificial intelligence (AI) and computational intelligence, primarily from machine learning and evolutionary computation, to design new methods and algorithms capable of constructing explanatory models for data produced by complex systems.
The primary objective of this project is to elaborate a new paradigm for modelling and analysis of complex systems that combines the advantages of the data-driven search, characteristic for machine learning, with the performance-driven search, characteristic for evolutionary computation. This will boil down to designing, implementing, and testing a family of new algorithms capable of producing models of complex systems that have desirable properties. In particular, this project is devoted to:
In this proposal we admit the definition of complex system that is widely accepted in the computational intelligence community. We define it as a system that is composed of many (often a large number of) components and exhibits some emergent behavior(s), i.e., behavior(s) that cannot be explained by considering the constituting components separately (Mitchell 2009). Other features often displayed by complex systems include:
Indirectly, these features determine the class of problems we would like to approach and the class of training data collections we would like the developed methods to be able to handle.
The central tenet of this project is that, given the recent progress in AI and computational intelligence, and the increasing amount of available computing power, it is both scientifically legitimate and meaningful to pursue the search for new methods that can efficiently discover models that explain complex systems while making moderate use of abstraction and domain-specific knowledge.
The proposed project will be carried out within a few main tasks detailed below.