Not all questions are asked with the same intention. Humans tend to address the implicit meaning of the question (that contributes to its pragmatic force), which requires knowledge of the context and a degree of common ground, more so than addressing the explicit propositional content of the question. Is recognizing the pragmatic force in today's human-robot dialogue systems worth the trouble? We focus on display questions (questions to which the asker already knows the answer) and argue that there are realistic human-robot interaction scenarios in existence today that would benefit from the deeper intention recognition. We also propose a method for obtaining display question annotations by embedding an elicitation question into the dialogue. The preliminary study of our robot receptionist shows that at least 16.7% of interactions with the embedded elicitation question include a display question.

Most information extraction research identifies the state of the world in text, including the entities and the relationships that exist between them. Much less attention has been paid to the understanding of dynamics, or how the state of the world changes over time. Because intelligent behavior seeks to change the state of the world in rational and utility-maximizing ways, common-sense knowledge about dynamics is essential for intelligent agents. In this paper, we describe a novel system, Prepost , that tackles the problem of extracting the preconditions and effects of actions and events, two important kinds of knowledge for connecting world state and the actions that affect it. In experiments on Web text, Prepost is able to improve by 79% over a baseline technique for identifying the effects of actions (64% improvement for preconditions).

Meta-cognitive skills to be developed in learning for the 21st century is the detection and correction of errors in solutions. These meta-cognitive skills can help to detect errors the learner has made her/himself as well as errors others have made. Our investigations in learning from errors have the ultimate goal to adapt the selection and presentation to the learner so that he/she can better learn from erroneous examples others have made. In our experiments we found that (1) erroneous examples with help provision can promote students skill of find errors, (2) the benefit from erroneous examples depends on the relation between the student's level and the example's difficulty, i.e. if the student is prepared for the problem, (3) for many students it is very difficult to correct errors.

As part of the "Survivor Buddy" project, we have created an open source speech translator toolkit which allows written or spoken word from multiple independent controllers to be translated into either a single synthetic voice, synthetic voices for each controller, orunchanged natural voice of each controller. The human controllers can work via the internet or be physically co-located with the Survivor Buddy robot. The toolkit is expected to be of use for exploring voice in general human-robot interaction. The Survivor Buddy project is motivated by our prior work which suggests that a trapped victim of a disaster, or other human who is dependent, will treat a rescue robot as a social medium and that the choice of robotic voice will be important. The robot will be both a medium to the "outside" world and a local, independent entity devoted to the victim Figure 1: View from the Survivor Buddy webcam with subpicture (e.g., a buddy).

A promising application area for proactive assistant agents is automated tutoring and training.  Intelligent tutoring systems (ITSs) assist tutors and tutees by automating diagnosis and adaptive tutoring. These tasks are well modeled by a partially observable Markov decision process (POMDP) since it accounts for the uncertainty inherent in diagnosis. However, an important aspect of making POMDP solvers feasible for real-world problems is selecting appropriate representations for states, actions, and observations. This paper studies two scalable POMDP state and observation representations. State queues allow POMDPs to temporarily ignore less-relevant states. Observation chains represent information in independent dimensions using sequences of observations to reduce the size of the observation set. Preliminary experiments with simulated tutees suggest the experimental representations perform as well as lossless POMDPs, and can model much larger problems.

Coarse word sense disambiguation (WSD) is an NLP task that is both important and practical: it aims to distinguish senses of a word that have very different meanings, while avoiding the complexity that comes from trying to finely distinguish every possible word sense. Reasoning techniques that make use of common sense information can help to solve the WSD problem by taking word meaning and context into account. We have created a system for coarse word sense disambiguation using blending, a common sense reasoning technique, to combine information from SemCor, WordNet, ConceptNet and Extended WordNet. Within that space, a correct sense is suggested based on the similarity of the ambiguous word to each of its possible word senses. The general blending-based system performed well at the task, achieving an f-score of 80.8\% on the 2007 SemEval Coarse Word Sense Disambiguation task.

In this paper, we describe an evaluation of the impact of embodiment, the effect of different kinds of embodiment, and the benefits of different aspects of embodiment, on direction-giving systems. We compared a robot, embodied conversational agent (ECA), and GPS giving directions, when these systems used speaker-perspective gestures, listener-perspective gestures and no gestures. Results demonstrated that, while there was no difference in direction-giving performance between the robot and the ECA, and little difference in participants’perceptions, there was a considerable effect of the type of gesture employed, and several interesting interactions between type of embodiment and aspects of embodiment.

An account of the labels commonly used to express themes could both help in assessing the coverage of models of narrative processing, and support recognizing themes by the textual appearance of these labels. This paper presents a preliminary analysis and catalog of thematic labels such as “vicious cycle” and “underdog”. In contrast to a top-down approach characterizing themes in terms of components of a model of narrative processing, a bottom-up approach is taken. Thematic labels are gathered independent of any particular model and they are catalogued according to the types of relationships the corresponding themes convey.

A new architectural framework for a metacognitive tutoring system is presented that is aimed to stimulate self-regulatory behavior in the learner.The new framework extends the cognitive architecture of TutorJ that has been already proposed by some of the authors. TutorJ relies mainly on dialogic interaction with the user, and makes use of a statistical dialogue planner implemented through a Partially Observable Markov Decision Process (POMDP). A suitable two-level structure has been designed for the statistical reasoner to cope with measuring and stimulating metacognitive skills in the user. Suitable actions have been designed to this purpose starting from the analysis of the main questionnaires proposed in the literature. Our reasoner has been designed to model the relation between each item in a questionnaire and the related metacognitive skill, so the proper action can be selected by the tutoring agent. The complete framework is detailed, the reasoner structure is discussed, and a simple application scenario is presented.

The KNEXT system produces a large volume of factoids from text, expressing possibilistic general claims such as that 'A PERSON MAY HAVE A HEAD' or 'PEOPLE MAY SAY SOMETHING'. We present a rule-based method to sharpen certain classes of factoids into stronger, quantified claims such as 'ALL OR MOST PERSONS HAVE A HEAD' or 'ALL OR MOST PERSONS AT LEAST OCCASIONALLY SAY SOMETHING' -- statements strong enough to be used for inference. The judgement of whether and how to sharpen a factoid depends on the semantic categories of the terms involved and the strength of the quantifier depends on how strongly the subject is associated with what is predicated of it. We provide an initial assessment of the quality of such automatic strengthening of knowledge and examples of reasoning with multiple sharpened premises.