Problem Solving and Complex Systems

TL;DR

This paper explores ant-based systems inspired by natural complex systems to solve problems like sequence alignment and language processing without evaluating solutions, focusing on emergent structures from system activity.

Contribution

It introduces a non-assessing ant-based approach for problem solving, modeling problems with graphs and observing resultant structures directly.

Findings

Effective for Multiple Sequences Alignment

Applicable to Natural Language Processing

Structures emerge without solution evaluation

Abstract

The observation and modeling of natural Complex Systems (CSs) like the human nervous system, the evolution or the weather, allows the definition of special abilities and models reusable to solve other problems. For instance, Genetic Algorithms or Ant Colony Optimizations are inspired from natural CSs to solve optimization problems. This paper proposes the use of ant-based systems to solve various problems with a non assessing approach. This means that solutions to some problem are not evaluated. They appear as resultant structures from the activity of the system. Problems are modeled with graphs and such structures are observed directly on these graphs. Problems of Multiple Sequences Alignment and Natural Language Processing are addressed with this approach.