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Artificial Intelligence: Natural Language
Abstract: This article provides an overview of modern dialog systems. We start by presenting the issues of voice recognition, language processing, dialog management, language generation and speech synthesis. Next, we analyze two robust speech-based interactive systems, NICE and TRIPS, examining how they solved each of the issues involved in spoken dialog processing. Finally, we examine the particulars of the game domain and provide suggestions on how to approach it, with illustrations from the case studies.
Abstract: This article presents the main techniques and paradigms of dialog management, with references to games, industrial applications and academic research. We cover dialog managers based on stacks, finite-state machines, frames, inference-engines and planners. For each technique, we point its strengths, applicability and issues when integrating into a broader dialog system in a game setting.
Fast and Accurate Gesture Recognition for Character Control |  |
Abstract: This article describes a simple, yet fast and accurate, way of gesture recognition that we have used in Punch'n'Crunch, a gesture-based fun-boxing game. The presented system is a very interesting way to control characters, but can also be used to recognize letters, numbers, and other arbitrary symbols. Gestures allow a more natural way for triggering a multitude of different commands.
Introduction to Single-Speaker Speech Recognition |
Designing a Vulgarity Filtering System |
SAPI: An Introduction to Speech Recognition |
Abstract: This article looks at providing newcomers to SAPI an easy-to-follow breakdown of how to get a simple SAPI application working. It looks briefly at setting up SAPI, how to construct the XML grammar files, handling SAPI messages and using the SAPI text-to-speech functionality. All these concepts are tied together using an demonstration application designed to make learning SAPI simple yet entertaining.
SAPI: Extending the Basics |
Abstract: This article extends upon the previous one by discussing concepts like dynamic grammar, additional XML grammar tags, altering voices and more SAPI events. The chapter uses a simple implementation of Go Fish! to demonstrate the concepts presented.
Conversational Agents: Creating Natural Dialogue between Players and Non-Player Characters |
Abstract: The quality of interactions between non-player characters (NPCs) and the player is an important area of Artificial Intelligence in games that is still in need of improvement. Game players frequently express that they want to see opponents and NPCs that appear to possess intelligence in games. However, most dialogue between players and NPCs in computer games is currently scripted, which does not add to the appearance of intelligence in the NPC. This article addresses these problems by giving an overview of NPCs in current games and presents a method called conversational agents, for improving dialogue between players and NPCs. Conversational agents are software agents that consist of models of personality and emotion, which allow them to demonstrate believable conversational behavior. The advantages of conversational agents include their ability to portray emotions and personality through dialogue. However, they also have disadvantage, in that they can be time consuming to develop.
This article will begin by discussing the conversational behavior of NPCs in current games. We will not be looking at the artificial intelligence (AI) capabilities of NPCs, only their ability to interact with the player. We will then discuss the components of a conversational agent - how to give it the appearance of personality and emotion. We will also look at the input that the agent needs to get from the environment, and what we want the agent to say to the player. We will conclude with the advantages and disadvantages of using conversational agents in games.
Practical Natural Language Learning |
Abstract: The perception of intelligence seems to be directly related to the observation of behavior that is surprising yet sensible. Natural language interfaces were common features of computer entertainment software prior to the advent of sophisticated computer graphics, but these were often repetitive in nature: encountering the same scripted conversation over and over again quickly becomes boring. Stochastic language models have the ability to acquire various features of a language from observations they make, and these features can be used generatively to produce novel utterances that have the properties of being both surprising and sensible. In this article we show how such a system, when used to host in-game socially-oriented conversations, can greatly contribute towards the subjective impression of intelligence experienced by the player.
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