Extending self-organizing particle systems to problem solving

Domain-Independent Lifelong Problem Solving Through Distributed ALife Actors

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, that is, to adapt rapidly to run-time changes in the problem domain, and to do it better than a standard, noncollective approach. DIAS therefore demonstrates a role for ALife in building scalable, general, and adaptive problem-solving systems.

Fusing Swarm Intelligence and Self-Assembly for Optimizing Echo State Networks

Optimizing a neural network's topology is a difficult problem for at least two reasons: the topology space is discrete, and the quality of any given topology must be assessed by assigning many different sets of weights to its connections. These two characteristics tend to cause very "rough." objective functions. Here we demonstrate how self-assembly (SA) and particle swarm optimization (PSO) can be integrated to provide a novel and effective means of concurrently optimizing a neural network's weights and topology. Combining SA and PSO addresses two key challenges. First, it creates a more integrated representation of neural network weights and topology so that we have just a single, continuous search domain that permits "smoother" objective functions. Second, it extends the traditional focus of self-assembly, from the growth of predefined target structures, to functional self-assembly, in which growth is driven by optimality criteria defined in terms of the performance of emerging structures on predefined computational problems. Our model incorporates a new way of viewing PSO that involves a population of growing, interacting networks, as opposed to particles. The effectiveness of our method for optimizing echo state network weights and topologies is demonstrated through its performance on a number of challenging benchmark problems.

An artificial ecosystem: emergent dynamics and lifelike properties

We discuss modeling and analysis of an artificial ecosystem. The ecosystem consists of basic elements, scents, plants, and animals. There are two species of animals: worms and beetles. As beetles absorb energy from worms, which absorb energy from blades of grass, which absorb energy from water, there is a food chain connecting animals to basic elements. The novelty of our approach lies in the modeling technique: we model the entire ecosystem using a single particle system. Consequently, the physical interaction dynamics not only shows emergent dynamics, but also some interesting lifelike properties. As the main contribution, we formalize the particle system and use it to model and analyze the ecosystem. We consider here several scenarios with nontrivial interaction dynamics.