BDI Agents for Real Time Strategy games

While the use of distributed intelligence has been incrementally spreading in the design of a great number of intelligent systems, the field of Artificial Intelligence in Real Time Strategy games has remained mostly a centralized environment. Despite turn-based games have attained AIs of world-class level, the fast paced nature of RTS games has proven to be a significant obstacle to the quality of its AIs.

Chapter 1 introduces RTS games describing their characteristics, mechanics and elements.

Chapter 2 introduces Multi-Agent Systems and the use of the Beliefs-Desires-Intentions abstraction, analysing the possibilities given by self-computing properties.

In Chapter 3 the current state of AI development in RTS games is analyzed highlighting the struggles of the gaming industry to produce valuable. The focus on improving multiplayer experience has impacted gravely on the quality of the AIs thus leaving them with serious flaws that impair their ability to challenge and entertain players.

Chapter 4 explores different aspects of AI development for RTS, evaluating the potential strengths and weaknesses of an agent-based approach and analysing which aspects can benefit the most against centralized AIs.

Chapter 5 describes a generic agent-based framework for RTS games where every game entity becomes an agent, each of which having its own knowledge and set of goals. Different aspects of the game, like economy, exploration and warfare are also analysed, and some agent-based solutions are outlined. The possible exploitation of self-computing properties to efficiently organize the agents activity is then inspected.

Chapter 6 presents the design and implementation of an AI for an existing Open Source game in beta development stage: 0 a.d., an historical RTS game on ancient warfare which features a modern graphical engine and evolved mechanics.

The entities in the conceptual framework are implemented in a new agent-based platform seamlessly nested inside the existing game engine, called ABot, widely described in Chapters 7, 8 and 9.

Chapter 10 and 11 include the design and realization of a new agent based language useful for defining behavioural modules for the agents in ABot, paving the way for a wider spectrum of contributors.

Chapter 12 concludes the work analysing the outcome of tests meant to evaluate strategies, realism and pure performance, finally drawing conclusions and future works in Chapter 13.