Abstract. We present a model of negotiation for virtual agents that extends previous work to be more human-like and applicable to a broader range of situations, including more than two negotiators with different goals, and negotiating over multiple options. The agents can dynamically change their negotiating strategies based on the current values of several parameters and factors that can be updated in the course of the negotiation. We have implemented this model and done preliminary evaluation within a prototype training system and a three-party negotiation with two virtual humans and one human. 1

We describe a text classification approach based on statistical language modeling. We show how this approach can be used for several natural language processing tasks in a virtual human system. Specifically, we show it can applied to language understanding, language generation, and character response selection tasks. We illustrate these applications with some experimental results. 1. natural language understanding (NLU) module that interprets the text of the user’s utterance and converts it into some internal representation; 2. dialog manager (DM) module that analyzes the interpretation and selects the appropriate response; 3. natural language generation (NLG) module that converts the internal representation to the text of the response. 1

This paper describes a new annotation GUI, called IORelator, which is designed to facilitate rapid semantic annotation in support of high-performance data-driven NLU for spoken dialogue systems. We summarize our requirements for rapid NLU annotation, and discuss how the GUI views and operations that IORelator provides meet these needs by enabling thousands of natural utterances to be quickly annotated with their correct semantics. 1

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