In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as 'Place the cup down (on the table)," potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.

Our learning by teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is structured so that successfully instructing their teachable agent requires the students to learn and understand science topics for themselves. This process is supported by adaptive scaffolding and feedback from the system. This feedback is instantiated through the interactions with the teachable agent and a mentor agent, named Mr. Davis. This paper provides an overview of two studies that were conducted with 5th grade science students and a description of the analysis techniques that we have developed for interpreting students’ activities in this learning environment.

Quantum theory has recently been employed to further advance thetheory of information retrieval (IR). A challenging research topicis to investigate the so called quantum-like interference in users'relevance judgment process, where users are involved to judge therelevance degree of each document with respect to a given query. Inthis process, users' relevance judgment for the current document isoften interfered by the judgment for previous documents, due to theinterference on users' cognitive status. Research from cognitivescience has demonstrated some initial evidence of quantum-likecognitive interference in human decision making, which underpins theuser's relevance judgment process. This motivates us to model suchcognitive interference in the relevance judgment process, which inour belief will lead to a better modeling and explanation of userbehaviors in relevance judgement process for IR and eventually leadto more user-centric IR models. In this paper, we propose to useprobabilistic automaton (PA) and quantum finite automaton (QFA),which are suitable to represent the transition of user judgmentstates, to dynamically model the cognitive interference when theuser is judging a list of documents.

This paper presents an anytime algorithm for  incremental intention recognition in a changing world.  The algorithm is performed by dynamically constructing the intention recognition model on top of a prior domain knowledge base. The model is occasionally reconfigured by situating itself in the changing world and removing newly found out irrelevant intentions. We also discuss some approaches to knowledge base representation for supporting situation-dependent model construction. Reconfigurable Bayesian Networks are employed to produce the intention recognition model.

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.

An important difficulty in developing spoken dialogue systems for robots is the open-ended nature of most interactions. Robotic agents must typically operate in complex, continuously changing environments which are difficult to model and do not provide any clear, predefined goal. Directly capturing this complexity in a single, large dialogue policy is thus inadequate. This paper presents a new approach which tackles the complexity of open-ended interactions by breaking it into a set of small, independent policies, which can be activated and deactivated at runtime by a dedicated mechanism. The approach is currently being implemented in a spoken dialogue system for autonomous robots.

We are motivated by building a system for an autonomous robot companion that collaborates with a human partner for achieving a common mission. The objective of the robot is to infer the human's preferences upon the tasks of the mission so as to collaborate with the human by achieving human's non-favorite tasks. Inspired by recent researches about the recognition of human's intention, we propose a unified model that allows the robot to switch accurately between verbal and non-verbal interactions. Our system unifies an epistemic partially observable Markov decision process (POMDP) that is a human-robot spoken dialog system aiming at disambiguating the human's preferences and an intuitive human-robot collaboration consisting in inferring human's intention based on the observed human actions. The beliefs over human's preferences computed during the dialog are then reinforced in the course of the task execution by the intuitive interaction. Our unified model helps the robot inferring the human's preferences and deciding which tasks to perform to effectively satisfy these preferences. The robot is also able to adjust its plan rapidly in case of sudden changes in the human's preferences and to switch between both kind of interactions. Experimental results on a scenario inspired from robocup@home outline various specific behaviors of the robot during the collaborative mission.

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.

Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoRP for classification framework, and develop it further for model selection problems in unsupervised learning where the main interest is to describe the associations between input measurements, like cluster analysis or graphical modelling. Theoretical properties and simulation studies are presented.

Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. A Bayesian network can thus be considered a mechanism for automatically applying Bayes' theorem to complex problems. In the application of Bayesian networks, most of the work is related to probabilistic inferences. Any variable updating in any node of Bayesian networks might result in the evidence propagation across the Bayesian networks. This paper sums up various inference techniques in Bayesian networks and provide guidance for the algorithm calculation in probabilistic inference in Bayesian networks.