DIAS: A Domain-Independent Alife-Based Problem-Solving System
This work addresses the challenge of building scalable, general, and adaptive problem-solving systems for AI and machine learning applications, though it appears incremental in its approach.
The authors introduced DIAS, a domain-independent problem-solving system based on Artificial Life principles, which uses a population of actors in a spatial medium to collectively solve problems without hyperparameter tuning and adapts rapidly to runtime changes, outperforming standard non-collective approaches.
A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only part of this medium, solves problems collectively in it. The process is independent of the domain and can be implemented through different kinds of actors. Through a set of experiments on various problem domains, DIAS is shown able to solve problems with different dimensionality and complexity, to require no hyperparameter tuning for new problems, and to exhibit lifelong learning, i.e. adapt rapidly to run-time changes in the problem domain, and do it better than a standard non-collective approach. DIAS therefore demonstrates a role for Alife in building scalable, general, and adaptive problem-solving systems.