DIAS: A Domain-Independent Alife-Based Problem-Solving System

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.Comment: 9 pages, 6 figure

Software Agent Evolution in Adaptive Agent Oriented Software Architecture

Adaptive Agent Oriented Software Architecture (AAOSA) is a new dynamic approach to software design based on multi-agent oriented architecture. Since the optimal agent organization is different from one environment to another, we proposed a distributed learning policy that is used in AAOSA for the purpose of agent organizational evolution. Knowing when and how to communicate and coordinate with other agents is an important efficiency and reliability question. In this paper, we propose the use of on-line feedback from users to motivate the learning whenever necessary, and show that distributing the evolution process over individual agent in AAOSA is efficient and reasonable