Rudolf Kadlec

Pogamut 3 Can Assist Developers in Building AI (Not Only) for Their Videogame Agents

Many research projects oriented on control mechanisms of virtual agents in videogames have emerged in recent years. However, this boost has not been accompanied with the emergence of toolkits supporting development of these projects, slowing down the progress in the field. Here, we present Pogamut 3, an open source platform for rapid development of behaviour for virtual agents embodied in a 3D environment of the Unreal Tournament 2004 videogame. Pogamut 3 is designed to support research as well as educational projects. The paper also briefly touches extensions of Pogamut 3; the ACT-R integration, the emotional model ALMA integration, support for control of avatars at the level of gestures, and a toolkit for developing educational scenarios concerning orientation in urban areas. These extensions make Pogamut 3 applicable beyond the domain of computer games.

Episodic memory for human-like agents and human-like agents for episodic memory

Episodic memory has been approached from many levels of analysis and many of its facets have been modeled computationally. Recently, several models of episodic memory have emerged in the domain of intelligent virtual agents (IVAs). Compared to neuro-/psychological models, their plausibility is limited. On the other hand, they can store representations of large environments and other complex memories over long-time intervals. This paper presents one such model and discusses the possibility of using IVAs as a test-bed to investigate neuro-/psychological models. The conclusion is that IVAs and their virtual environments can constitute an ecologically plausible framework allowing for study and integration of the neuro-/psychological models.

Towards Fast Prototyping of IVAs Behavior: Pogamut 2

We present the platform for IVAs development in the human like environment of the first-person shooter game Unreal Tournament 2004. This environment is extendible and supported by vast community of users. Based on our previous experience the problem of fast verification of models of artificial intelligence or IVAs is in implementation issues. The developer spends most of his time solving technical environment dependent issues and malfunctions, which drives him away from his goals. Therefore our modular platform provides a tool, which helps solving those problems and the developer can spend saved time by solving another AI based issues and model verification. The platform is aimed for research and educational purposes.

Emohawk: Searching for a Good Emergent Narrative

We report on the progress we have achieved in development of Emohawk, a 3D virtual reality application with an emergent narrative for teaching high-school students and undergraduates the basics of virtual characters control, emotion modelling, and narrative generation. Besides, we present a new methodology, used in Emohawk, for purposeful authoring of emergent narratives of Facades complexity. The methodology is based on massive automatic search for stories that are appealing to the audience whilst forbidding the unappealing ones during the design phase.

Timing in episodic memory for virtual characters

Recently several episodic memory models have been developed for virtual characters to increase their believability. However, none of these models addresses the issue of plausible timing of events. Here we present a model that addresses this issue. We introduce a prototype implementation and discuss the psychological underpinnings. Then we demonstrate that the model is able to mimic some psychological phenomena such as blending similar episodes.

3D Immersion in Virtual Agents Education

Many projects featuring intelligent virtual agents have emerged in last years, but not many reports on their advances in education. This paper presents the curricula of a university course on Modelling Behaviour of Human and Animal-like Agents, including a seminar in which students develop their own virtual agents using a toolkit we have developed. This course has been also scaled for a workshop with computer science high-school students. An evaluation of the course is presented and main lessons learned overviewed. The paper also explicitly formulates the teaching methodology underpinning the course and outlines several general questions hoping to start a broader discussion on educational issues related to the field of intelligent virtual agents.

Extensions and Applications of Pogamut 3 Platform

We present recent extensions for the intelligent virtual agent development platform Pogamut 3. These extensions are: the agent coordination language StorySpeak designed for the purposes of storytelling applications, support for gestures based on BML, the visual editor of reactive plans, connection of the cognitive architecture ACT-R, an educational application aimed at orientation in urban areas and finally an emotional model.

StoryFactory --- a tool for scripting machinimas in unreal engine 2 and UDK

As part of our broader initiative on promoting the education in the field of computer science and ICT at high schools and universities, we have created the StoryFactory tool, which enables students to script short movies in a 3D virtual world. In an engaging way, StoryFactory introduces challenges posed by scripting 3D virtual characters and screenwriting. The tool is supposed to be used in ICT and/or media education classes. Here, we present the tool along with first results from its evaluations.

DyBaNeM: Bayesian Episodic Memory Framework for Intelligent Virtual Agents

Episodic Memory (EM) abilities are important for many types of intelligent virtual agents (IVAs). However, the few IVA–EM systems implemented to date utilize indexed logs of events as the underlying memory representation, which makes it hard to model some crucial facets of human memory, including hierarchical organization of episodes, reconstructive memory retrieval, and encoding of episodes with respect to previously learnt schemata. Here, we present a new general framework for EM modeling, DyBaNeM, which capitalizes on bayesian representation and, consequently, enables modeling these (and other) features easily. By means of a proof-of-concept implementation, we demonstrate that our approach to EM modeling is promising, at least for domains of moderate complexity.

Creating game bots in a few easy steps

This paper describes the content of our tutorial concerned with the development of artificial intelligence for virtual characters in computer games, so-called bots. The Pogamut platform was chosen for this tutorial.