Abstract
Developing an intelligent player for a game is no easy task. Each Artificial Intelligent Player is created specifically for each context with very little re-usability. However, some lower level developments have great significance in both games and other areas — such as the search, pathing, or optimization algorithms. In this work, we designed and implemented an algorithm that combined Swarm Intelligence concepts with the traditional decision mechanisms of modern Artificial Intelligent Players — specifically those used in Strategy Games. Our main objective was to assert the adequacy of Swarm Intelligence current knowledge to the Artificial Intelligent Player requirements, followed by the development of a test algorithm in itself. The basic concept was to distance our implementation from a common centralized solution, into a decentralized solution, and complementing it by applying some of the currently documented Swarm Intelligence notions. The resulting algorithm was especially responsible for the means of communication between the units of an Artificial Intelligent Player. A centralized and scripted Artificial Intelligence was used as benchmark for our Swarm Intelligence based solution. This work is an attempt to answer the problems resulting from predictable Artificial Players — a common issue with scripted implementations — and to improve its adaptability — taking advantage of the emergent behavior resulting of the Swarm Intelligence concepts.