The eleven articles in this special issue focus on the use of computational modeling in developing the narratives for video programs and online games. Narratives are perceived to be central to cultures, to the ways people communicate, and, many have argued, to cognition itself. The articles in this issue explore these issues and reports on technologies and computer applications that support narrative programming.

In this paper, I will discuss a set of techniques for supporting limited variable binding in behavior-based systems. This adds additional useful expressivity while preserving the property of selecting actions in 0(1) time and space. An unusual feature of the method is that it allows inference operations to be reduced to bit-parallel and short-vector operations, making it particularly well suited to real-time operation on modern superscalar and SIMD architectures. I also describe an implementation of the technique in the Twig procedural animation system.

In this paper, we describeTwig, a fast, AI-friendly procedural animation system that supports easy authoring of new behaviors. The system provides a simplified dynamic simulation that is specifically designed to be easy to control. Characters are controlled by applying external forces directly to body parts, rather than by simulating joint torques. This ldquopuppetry-stylerdquo of control provides the simplicity of kinematic control within an otherwise dynamic simulation. Although less realistic than motion capture or full biomechanical simulation,Twigproduces compelling, responsive character behavior. Moreover, it is fast, stable, supports believable physical interactions between characters such as hugging, punching, and dragging, and makes it easy to author new behaviors.

Lighting design is an essential element of visual storytelling. In cinematic theory, lighting design plays an important role in illumination, directing viewer's gaze to important locations, and portraying visual tension. However, lighting design is a complex process, and is especially problematic for interactive entertainment systems, since important design parameters such as spatial configuration, dramatic focus, and dramatic intensity, cannot be determined in advance. Indeed, they often change dramatically during interaction. Additionally, manually adjusting colors, positions, and angles of each light in a scene is a time consuming and tedious process. In this paper, we describe a system developed based on cinematic and theatrical lighting design theory to automatically adjust positions, colors, and angles of each light in real-time to accommodate the continually evolving dramatic situation, while maintaining the desired style, and ensuring visual continuity.

We discuss the importance of, and the prospect for, areas of mobile robot specification. The goal is to start a technical discussion in the mobile robotics community to create reusable building blocks that result in reliable, affordable robotics products are discussed. The form of this discussion is a roadmap for specifying building blocks for mobile robots.

Most physically implemented multi-robot controllers are based on extensions of behavior-based systems. While efficient, such techniques suffer from a paucity of representational power. Symbolic systems, on the other hand, have more sophisticated representations but are computationally complex and have model coherency issues. We describe HIVEMind, a tagged behavior-based architecture for small teams of cooperative robots. In HIVEMind, robots share inferences and sensory data by treating other team members as virtual sensors connected by wireless links. A representation based on bit-vectors allows team members to share intentional, attentional, and sensory information using relatively low-bandwidth connections. We describe an application of the architecture to the problem of systematic spatial search.

We argue that the analysis of agent/environment interactions should be extended to include the conventions and invariants maintained by agents throughout their activity. We refer to this thicker notion of environment as a lifeworld and present a partial set of formal tools for describing structures of lifeworlds and the ways in which they computationally simplify activity. As one specific example, we apply the tools to the analysis of the Toast system and show how versions of the system with very different control structures in fact implement a common control structure together with different conventions for encoding task state in the positions or states of objects in the environment.

"Software architectures for hardware agents." Journal of Experimental & Theoretical Artificial Intelligence, 9(2-3), p. 145

Traditional architectures have fundamental epistemological problems. Perception is inherently resource limited so controlling perception involves all the same AI-complete problems of reasoning about time and resources as the full-scale planning problem. Allowing a planner to transparently assume that the information it needs will automatically be present and up-to-date in the model thus presupposes a solution to a problem at least as difficult as planning itself. Although one can imagine many possible solutions to this problem, such as allowing the planner to recurse on its own epistemological problems, there have been no convincing attempts at this. In this paper, I compare behaviour-based and traditional systems in terms of their representational power and the strengths of their implicit epistemological theories. I argue that both have serious limitations and that those limitations are not addressed simply by joining the two into a hybrid. I discuss my work with using vision to support real-time activity and give an example of an interesting intermediate point between reactive and classical architectures that preserves the simplicity and parallelism of behaviour-based systems while supporting ‘symbolic’ representations. Traditionally, AI theories have assumed, either implicitly or explicitly, an architecture in which modules of the mind (perception, reasoning, motor control, etc.) are linked by way of some centralized database like structure, often referred to as a world model. Recently, a number of alternative architectures have been proposed which, to greater or lesser degrees, claim to do away with world models or with representations entirely. Many of the criticisms of traditional architectures revolve around speed and timescale. Planning, so the story goes, is slow but flexible, while feedback loops are fast but stupid. A common approach, both in this special issue and in the literature in general, is to adopt a hybrid which fuses a slow planner running on a long time-scale and a set of fast feedback loops running on a short time-scale. The problem with this argument is that planning is not slow, it is combinatorially explosive. Running an O(2 n ) algorithm on a time-scale ten times slower is the same as running it on a computer ten times faster : it simply lets one increase n by three. If time-scale were the true problem, faster CPUs would make tiered architectures obsolete in a few years. I believe the true issues are not speed, in the sense of time-scale, but combinatorics and epistemology. The former has been extensively discussed, so I will focus on epistemology. Clearly, if an agent architecture is to be successful it must take into account the capacities and limitations of perception. In this paper I discuss the influence of perceptual architecture on agent architecture, argue that the recent wave of tiered architectures do not adequately address these problems, and discuss my work on using vision to support real-time activity.

Visual collision avoidance involves two difficult subproblems: obstacle recognition and depth measurement. We present a class of algorithms that use particularly simple methods for each subproblem and derive a set of sufficient conditions for their proper functioning based on a set of idealizations. We then discuss and compare two different implementations of the approach on mobile robots and discuss their performance. Finally, we experimentally validate the idealizations.>

A method is presented for recovering the rotation of a moving object using both velocity and acceleration. Acceleration is a useful form of information to incorporate into a structure-from motion system. It allows the use of clustering algorithms for recovering the motion parameters of a rotating object, which in turn allows the weakening of the spatial continuity constraint to a spatial coherence constraint, together with a temporal continuity constraint. This allows the integration of information over both time and space without limiting assumptions about surface structure.>