Towards reactive navigation and attention skills for 3D intelligent characters

This paper presents a neural design which is able to provide the necessary reactive navigation and attention skills for 3D embodied agents (virtual humanoids or characters). Based on Grossberg’s neural model of conditioning [6], as recently implemented by Chang and Gaudiando [7], and according to the Adaptative Resonance Theory (ART) and the neuroscientific concepts associated, the neural design introduced has been divided in two main phases. Firstly, an environmentcategorization phase, where an on-line pattern recognition and categorization of the current agent sensory input data is carried out by a self organizing neural network, which will finally provide the agent’s short term memory layer(STM). Secondly, and based on the classical conditioning paradigm, the model will associate the interesting STM states, from the navigation or attention points of view, to finally simulate these necessary skills for 3D characters or humanoids. Finally, we will show some experimental navigational results, through the integration of the model presented in 3D virtual environments.Partially supported by the GVA-project CTIDIB-2002-182 (Spain)

Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Dynamic behavior-based control and world-embedded knowledge for interactive artificial intelligence

Video game designers depend on artificial intelligence to drive player experience in modern games. Therefore it is critical that AI not only be fast and computation- ally inexpensive, but also easy to incorporate with the design process. We address the problem of building computationally inexpensive AI that eases the game de- sign process and provides strategic and tactical behavior comparable with current industry-standard techniques. Our central hypothesis is that behavior-based characters in games can exhibit effec- tive strategy and coordinate in teams through the use of knowledge embedded in the world and a new dynamic approach to behavior-based control that enables charac- ters to transfer behavioral knowledge. We use dynamic extensions for behavior-based subsumption and world-embedded knowledge to simplify and enhance game character intelligence. We find that the use of extended affordances to embed knowledge in the world can greatly reduce the effort required to build characters and AI engines while increasing the effectiveness of the behavior controllers. In addition, we find that the technique of multi-character affordances can provide a simple mechanism for enabling team coordination. We also show that reactive teaming, enabled by dynamic extensions to the subsumption architecture, is effective in creating large adaptable teams of characters. Finally, we show that the command policy for reactive teaming can be used to improve performance of reactive teams for tactical situations

Emotions, behaviour and belief regulation in an intelligent guide with attitude

Abstract unavailable please refer to PD