Swen E. Gaudl

Extended ramp goal module: Low-cost behaviour arbitration for real-time controllers based on biological models of dopamine cells

The current industrial focus in virtual agents and digital games is on complex systems that more accurately simulate the real world, including cognitive characters. This trend introduces a multitude of control parameters generally accompanied by high computational costs. The resulting complexity limits the applicability of AI in these domains. One solution to this problem is to focus on light-weight flexible AI architectures which can be simultaneously generated, controlled and run in parallel. The resulting systems should then be able to control individual game characters, scaling up to large numbers of characters, forming even complex social systems. Here we contribute one element of such a system: a light-weight systems-engineering approach for enriching behaviour arbitration in action selection. Our mechanism-ERGo-improves high-level goal arbitration in existing light-weight action selection mechanisms. ERGo provides easy and reliable non-deterministic control of goal switching, activation and inhibition, allowing natural behaviour maintenance. This mechanism can aid agent design in cases where static, linear, predefined priorities are undesirable. The model underlying our approach is biomimetic, based on neuro-cognitive research on the dopaminic cells responsible for controlling goal switching and maintenance in the mammalian brain. We demonstrate and evaluate our mechanism in a real-time, game-like simulation environment, using a previously-published system as a baseline for comparison. We demonstrate that ERGo is effective, and betters the previous approach.

Learning from Play: Facilitating Character Design Through Genetic Programming and Human Mimicry

Mimicry and play are fundamental learning processes by which individuals can acquire behaviours, skills and norms. In this paper we utilise these two processes to create new game characters by mimicking and learning from actual human players. We present our approach towards aiding the design process of game characters through the use of genetic programming. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. Computational creativity approaches this issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks. Our GP approach to this problem not only mimics actual human play but creates character controllers which can be further authored and developed by a designer. This keeps the designer in the loop while reducing repetitive labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework and preliminary results supporting our claim.

Emotionally Driven Robot Control Architecture for Human-Robot Interaction

With the increasing demand for robots to assist humans in shared workspaces and environments designed for humans, research on human-robot interaction (HRI) gains more and more importance. Robots in shared environments must be safe and act in a way understandable by humans, through the way they interact and move. As visual cues such as facial expressions are important in human-human communication, research on emotion recognition, expression, and emotionally enriched communication is of great importance to HRI and has also gained increasing attention during the last two decades [1, 3, 4, 6, 8]. Most of the existing work focuses on the recognition of human emotions or mimicking their expression [1, 6] and emotional action selection [3, 5]. Less work is done on the role of emotions in influencing human behaviour in HRI [4].

Rapid Game Jams with Fluidic Games: A User Study & Design Methodology

Abstract We introduce rapid game jams, a style of game jam that takes only 1–2 h and is focused on design experimentation rather than on programming and technical implementation. To support that kind of rapid game-design experimentation, we have designed a class of games that we call fluidic games. These are mobile games in which the game mechanics and other aspects of the games are editable on the fly, directly on the device, allowing for frequent play/design context shifts. We have conducted four rapid game jams with 105 participants from a local Girlguiding organisation, in order to gain real-world experience with this concept. We analyse results from a survey instrument completed by 69 participants in two of these rapid game jams. In order to guide future work in addressing questions left open by this study, we did a qualitative analysis of the designed games to gain additional insights into participants’ design practice.

Curious users of casual creators

Casual creators are a type of design tool identified by Compton & Mateas, characterised by an orientation towards enjoyable, intrinsically motivated creative exploration, rather than task-oriented designer productivity. In our experiments holding rapid game jams with Wevva, a casual creator for mobile game design, we have noticed, however, that users seem to vary considerably even within the context of using a casual creator. Some people focus on designing specific games, while others explore the design space extensively, or even focus exclusively on prodding the edges of the design space looking for its possibilities and limits. We hypothesise that the latter group of users is driven primarily by curiosity about a casual creator and its design space. This results in different patterns of behaviour to the former group (of design-oriented users), which may worth characterising and perhaps explicitly designing for.

The extended ramp model: A biomimetic model of behaviour arbitration for lightweight cognitive architectures

Abstract In this article, we present an idea for a more intuitive, low-cost, adjustable mechanism for behaviour control and management. One focus of current development in virtual agents, robotics and digital games is on increasingly complex and realistic systems that more accurately simulate intelligence found in nature. This development introduces a multitude of control parameters creating high computational costs. The resulting complexity limits the applicability of AI systems. One solution to this problem it to focus on smaller, more manageable, and flexible systems which can be simultaneously created, instantiated, and controlled. Here we introduce a biologically inspired systems-engineering approach for enriching behaviour arbitration with a low computational overhead. We focus on an easy way to control the maintenance, inhibition and alternation of high-level behaviours (goals) in cases where static priorities are undesirable. The models we consider here are biomimetic, based on neuro-cognitive research findings from dopaminic cells responsible for controlling goal switching and maintenance in the mammalian brain. The most promising model we find is applicable to selection problems with multiple conflicting goals. It utilizes a ramp function to control the execution and inhibition of behaviours more accurately than previous mechanisms, allowing an additional layer of control on existing behaviour prioritization systems.