Building Robust Real-Time Game AI
Games are a part of our modern culture and Game AI is the mechanism giving them life. During my PhD I was interested in understanding and aiding the design process of behaviour-based AI systems or more specific character AI. Within this post I want to discuss crucial parts of my PhD and the reasoning process behind it. The intended process will start of with elements from my thesis but will discuss the topic more broadly.
Digital games are part of our culture and have gained significant attention over the last decade. The growing capabilities of home computers, gaming consoles and mobile phones allow current games to visualise 3D virtual worlds, photo-realistic characters and the inclusion of complex physical simulations. The growing computational power of those devices enables the usage of complex algorithms while visualising data. Therefore, opportunities arise for developers of interactive products such as digital games which introduce new, challenging and exciting elements to the next generation of highly interactive software systems. Two of those challenges, which current systems do not address adequately, are design support for creating Intelligent Virtual Agents and more believable non-player characters for immersive game-play.
We start in this thesis by addressing the agent design support first and then extend the research, addressing the second challenge. The main contributions of this thesis are:
- The Posh-sharp sytem is a framework for the development of game agents. The platform is modular, extendable, offers multi-platform support and advanced software development features such as behaviour inspection and behaviour versioning. The framework additionally integrates an advanced information exchange mechanism supporting loose behaviour coupling.
- The Agile behaviour design methodology integrates agile software development and agent design. To guide users, the approach presents a work-flow for agent design and guiding heuristics for their development.
- The action selection augmentation ERGo introduces a "white-box" solution to altering existing agent frameworks, making their agents less deterministic. It augments selected behaviours with a bio-mimetic memory to track and adjust their activation over time.
With the new approach to agent design, the development of deeper agent behaviour for digital adversaries and advanced tools supporting their design is given. Such mechanisms should enable developers to build robust non-player characters that act more human-like in an efficient and robust manner. Within this thesis, different strategies are identified to support the design of agents in a more robust manner and to guide developers. These discussed mechanisms are then evolved to develop and design Intelligent Virtual Agents (IVA). Because humans are still the best measurement for human-likeness, the evolutionary cycle involves feedback given by human players.
to be continued ...