Intelligent Complex Networks

TL;DR

This paper explores how complex networks can serve as artificial intelligence mechanisms for puzzle assembly, analyzing various network types, initial distributions, and buffering strategies to optimize performance.

Contribution

It introduces the concept of using complex networks as AI engines for puzzle assembly and evaluates different network structures and parameters for optimal results.

Findings

BA-based engines are the fastest in puzzle assembly.

Eigenvector centrality distribution yields the best performance.

Buffer sizes proportional to node degree improve efficiency.

Abstract

The present work addresses the issue of using complex networks as artificial intelligence mechanisms. More specifically, we consider the situation in which puzzles, represented as complex networks of varied types, are to be assembled by complex network processing engines of diverse structures. The puzzle pieces are initially distributed on a set of nodes chosen according to different criteria, including degree and eigenvector centrality. The pieces are then repeatedly copied to the neighboring nodes. The provision of buffering of different sizes are also investigated. Several interesting results are identified, including the fact that BA-based assembling engines tend to provide the fastest solutions. It is also found that the distribution of pieces according to the eigenvector centrality almost invariably leads to the best performance. Another result is that using the buffer sizes proportional to the degree of the respective nodes tend to improve the performance.