Frederick W. P. Heckel

Influence points for tactical information in navigation meshes

Good artificial intelligence for strategy and first person shooter games requires tactical information. Tactical information assists agents in choosing appropriate places to place vulnerable resources, safe paths for moving through the world, and the most effective places to attack the enemy. Unfortunately, providing this information can be very expensive, as it typically requires keeping one or more full map arrays around. In this paper, we present influence points, a technique for providing tactical information by annotating navigation meshes. This greatly improves the memory cost of storing the information, and reduces the cost of updates as the world changes.

Representational complexity of reactive agents

Reactive agents are an important part of video games and numerous tools have emerged to facilitate the rapid construction of such agents. While the ability of the commonly used reactive techniques to express agent specifications is roughly equivalent, the authorial burden of constructing these specifications varies. In practice, this means that identical agent behavior may be more difficult to create in some architectures than others. In this paper we introduce the notion of representational complexity that relates to the authorial burden of constructing such agents and theoretically compare the representational complexity of finite state machines, behavior trees, and subsumption architectures. Our key finding is that hierarchical subsumption architectures have significantly lower representational complexity as compared to hierarchical finite state machines and behavior trees, which makes subsumption the best choice when developing authoring tools for non-expert users.

Embedding Information into Game Worlds to Improve Interactive Intelligence

Current game worlds are visually rich but information poor – particularly poor from the artificial intelligence (AI) point of view. Where the player sees a rich visual representation of 3D objects, internally these are just very sparsely described collections of points in space. Tools for advanced world creation, character modeling, animation, and advancements in computer graphics have brought us into the age of near photo-realistic interaction; however, these interactions are still very limited in comparison to the real world, and the information is presented overwhelmingly for the player, packaged for the Graphics Processing Unit (GPU) with little reflection or structure suitable for use by AI systems. This problem of a lack of rich information suitable for consumption by the game AI often limits the true potential for deeper levels of interaction that are becoming more in-demand by game players. This chapter presents a number of tools and techniques, which are being used to improve the embedded information contained in immersive game worlds. Symbolic annotation of the environmental elements, advanced spatial decomposition, calculating the information value of the surfaces in an interactive environment, and visual analysis form the core tools and information generators of our Common Games Understanding and Learning (CGUL) Toolkit. Using these tools to incorporate information into the game design and development process can help create information-rich interactive worlds. AI developers can work with these environmental information elements to improve non-player character (NPC) interactions both with the player and the environment, enhancing interaction, and leading to new possibilities such as meaningful in-game learning and character portability. Case studies from two different projects using these techniques provide some additional insight and reference as to how these techniques have been incorporated into current game AI and research.

Contextual affordances for intelligent virtual characters

Building artificial intelligence for games is an extremely involved process, and the cost of developing AI for minor characters may be greater than the payoff in increased immersion. Affordances, used to create smart objects, reduce the complexity of character controllers. While affordances improve the complexity of individual controllers, the technique does not take advantage of environmental information to reduce the behavioral decision space. We present contextual affordances, which extend basic affordances to use context from object, agent, and environmental state to present only the most relevant actions to characters. We show how contextual affordances improve even random-decision agents, and discuss the complexity of building contextual objects.

Failure detection and reactive teaming for behavior-based subsumption

Fast, lightweight intelligent controllers make it possible to apply agent-based artificial intelligence to even highly resource-constrained systems. Reactive controlmethods can provide this capability, and are frequently used in applications such as real-time games and robotics. Unfortunately, reactive controllers may not deal well with errors, as they cannot fall back on a planner to deal with unexpected situations. This limitation makes them more susceptible to certain types of failures than deliberative techniques. In this paper, we describe how four major types of failures can be detected in reactive controllers. We show how this can lead to extensions for behavior-based subsumption which allow more robust single agents, enable inexpensive multi-agent coordination, and improve agent design tools.