Declaring Local Contexts of Words with Extensible Dependency Grammar

Extensible dependency grammar (XDG) is a modern formalism for declaring dependency relations between lexical entries, generally used to construct natural language parsers. This work shows how to use XDG to declare specific contexts of the words, thus turning XDG parser into a word sense disambiguation module or a contextsensitive bilingual dictionary. The capabilities of the proposed method are shown on the example of small English to Finnish dictionary, helpful for entry-level Finnish language learners

Building a Believable and Effective Agent for a 3D Boxing Simulation Game

Abstract-This paper describes an approach used to build and optimize a practical AI solution for a 3D boxing simulation game. The two main features of the designed AI agent are believability (human-likeness of agent's behavior) and effectiveness (agent's capability to reach own goals). We show how learning by observation and case-based reasoning techniques are used to create believable behavior. Then we employ reinforcement learning to optimize agent's behavior, turning the agent into a strong opponent, acting in a commercial-level game environment. The used knowledge representation scheme supports high maintainability, important for game developers. Keywords-believability; behavior capture; learning by observation; reinforcement learning. INTRODUCTION The quality of a virtual agent is usually associated with its effectiveness in reaching own goals. In these terms, an agent that plays chess at grandmaster level is better than an average-skilled AI player. However, in the domain of computer simulation and video games, the factor of believability [1] also turns out to be one of the key factors of a successful AI. According to [1], a believable agent "provides the illusion of life, and thus permits the audience's suspension of disbelief". Such an agent normally possesses certain human-like features: it can learn, make mistakes, and adjust own strategy to be effective against a particular opponent. Believability is recognized as an important factor both by researchers and by game developers [2; 3]. A number of recent research projects are devoted to the creation of believable agents for video game environments In our recent wor

Improving POS Tagging for Ungrammatical Phrases

ABSTRACT Modern part-of-speech (POS) tagging tools can provide high quality markup for grammatically correct documents, but ungrammatical sentences can be challenging for them. In the present paper we study the problem of POS-tagging for the texts that contain grammatical errors, and show how POS-taggers can be improved for the use in this context. Specifically, we propose to include ungrammatical POS-tagged sentences into the text corpus used to train a tagger (presumably, a tagger is based on a certain variation of machine learning)