Antonio Roque

The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing

The Why2-Atlas system teaches qualitative physics by having students write paragraph-long explanations of simple mechanical phenomena. The tutor uses deep syntactic analysis and abductive theorem proving to convert the students essay to a proof. The proof formalizes not only what was said, but the likely beliefs behind what was said. This allows the tutor to uncover misconceptions as well as to detect missing correct parts of the explanation. If the tutor finds such a flaw in the essay, it conducts a dialogue intended to remedy the missing or misconceived beliefs, then asks the student to correct the essay. It often takes several iterations of essay correction and dialogue to get the student to produce an acceptable explanation. Pilot subjects have been run, and an evaluation is in progress. After explaining the research questions that the system addresses, the bulk of the paper describes the systems architecture and operation.

A Virtual Tour Guide for Virtual Worlds

In this paper we present an implementation of a embodied conversational agent that serves as a virtual tour guide in Second Life. We show how we combined the abilities of a conversational agent with navigation in the world and present some preliminary evaluation results.

Degrees of Grounding Based on Evidence of Understanding

We introduce the Degrees of Grounding model, which defines the extent to which material being discussed in a dialogue has been grounded. This model has been developed and evaluated by a corpus analysis, and includes a set of types of evidence of understanding, a set of degrees of groundedness, a set of grounding criteria, and methods for identifying each of these. We describe how this model can be used for dialogue management.

A Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals

In this paper, we explore the problem of selecting appropriate interventions for students based on an analysis of their interactions with a tutoring system. In the context of the WHY2 conceptual physics tutoring system, we describe CarmelTC, a hybrid symbolic/statistical approach for analysing conceptual physics explanations in order to determine which Knowledge Construction Dialogues (KCDs) students need for the purpose of encouraging them to include important points that are missing. We briefly describe our tutoring approach. We then present a model that demonstrates a general problem with selecting interventions based on an analysis of student performance in circumstances where there is uncertainty with the interpretation, such as with speech or text based natural language input, complex and error prone mathematical or other formal language input, graphical input (i.e., diagrams, etc.), or gestures. In particular, when student performance completeness is high, intervention selection accuracy is more sensitive to analysis accuracy, and increasingly so as performance completeness increases. In light of this model, we have evaluated our CarmelTC approach and have demonstrated that it performs favourably in comparison with the widely used LSA approach, a Naive Bayes approach, and finally a purely symbolic approach.

Varying Personality in Spoken Dialogue with a Virtual Human

This poster reports the results of two experiments to test a personality framework for virtual characters. We use the Tactical Questioning dialogue system architecture (TACQ) [1] as a testbed for this effort. Characters built using the TACQ architecture can be used by trainees to practice their questioning skills by engaging in a role-play with a virtual human. The architecture supports advanced behavior in a questioning setting, including deceptive behavior, simple negotiations about whether to answer, tracking subdialogues for offers/threats, grounding behavior, and maintenance of the affective state of the virtual human. Trainees can use different questioning tactics in their sessions. In order for the questioning training to be effective, trainees should have experience of interacting with virtual humans with different personalities, who react in different ways to the same questioning tactics.

Using virtual tour behavior to build dialogue models for training review

We develop an intelligent agent that builds a user model of a learner during a tour of a virtual world. The user model is based on the learners answers to questions during the tour. A dialogue model for a simulated instructor is tailored to the individual learner based upon this user model. We describe an evaluation to track system accuracy and user perceptions.