  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.
