Many human social exchanges and coordinated activities critically involve dialogue interactions. Hence, we need to develop natural humanlike dialogue processing mechanisms for future robots if they are to interact with humans in natural ways. In this article we discuss the challenges of designing such flexible dialogue-based robotic systems. We report results from data we collected in human interaction experiments in the context of a search task and show how we can use these results to build more flexible robotic architectures that are starting to address the challenges of task-based humanlike natural language dialogues on robots.

While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. We compare its performance variations form a knowledge base from which to a teleoperated robot following a scripted task we learn what to say and what actions to perform to protocol and examine both the behavior of the achieve certain goals.

If human-robot interaction is mainly shaped by users’ strategies to deal with their unfamiliar artificial com¬munication partner, as it is suggested here, robot dialog design should orient at reducing users’ uncertainty about the affordances of the robot and the joint task. Two experiments are presented that investigate the impact of verbal robot utterances on users’ behavior; results show that users react sensitively to subtle linguistic cues that may guide them into appropriate understandings of the robot. Furthermore, the role of user expectations and robot appearance are discussed in the light of the model presented.

Of all computational systems, robots are unique in their ability to afford embodied interaction using the wider range of human communicative cues. Research on human communication provides strong evidence that embodied cues, when used effectively, elicit social, cognitive, and task outcomes such as improved learning, rapport, motivation, persuasion, and collaborative task performance. While this connection between embodied cues and key outcomes provides a unique opportunity for design, taking advantage of it requires a deeper understanding of how robots might use these cues effectively and the limitations in the extent to which they might achieve such outcomes through embodied interaction. This article aims to underline this opportunity by providing an overview of key embodied cues and outcomes in human communication and describing a research program that explores how robots might generate high-level social, cognitive, and task outcomes such as learning, rapport, and persuasion using embodied cues such as verbal, vocal, and nonverbal cues.

Believability of characters has been an objective in literature, theater, film, and animation. We argue that believable robot characters are important in human-robot interaction, as well. In particular, we contend that believable characters evoke users’ social responses that, for some tasks, lead to more natural interactions and are associated with improved task performance. In a dialogue-capable robot, a key to such believability is the integration of a consistent storyline, verbal and nonverbal behaviors, and sociocultural context. We describe our work in this area and present empirical results from three robot receptionist testbeds that operate "in the wild."

In parallel with these efforts, significant advances have also been made in robotics. Innovations in sensing, reasoning, and manipulation have allowed autonomous robots to move beyond the walls of computing labs into the workplace, home, and street. Bringing robots into real-world environments has made it clear to researchers that robots need not only accurately navigate and manipulate objects, but also to work alongside and, ultimately, interact and collaborate with humans. Subsequently, efforts at the intersection of spoken dialogue and human-robot interaction (HRI) have sought to broaden studies of spoken dialogue to richer, more natural, physically situated settings, and have brought to the fore the rich research area of situated dialogue, focused on challenges and opportunities at the intersection of natural language, robotics, and commonsense reasoning. Projects in this realm have addressed challenges with the use of dialogue as enabling coordination among multiple actors, taking into consideration not only the details of the task at hand, but also the dynamic physical and social context in which the actors are immersed and the affordances that embodiment provides. This special issue of AI Magazine on dialogue with robots brings together a collection of articles on situated dialogue.

Most classification methods are based on the assumption that data conforms to a stationary distribution. The machine learning domain currently suffers from a lack of classification techniques that are able to detect the occurrence of a change in the underlying data distribution. Ignoring possible changes in the underlying concept, also known as concept drift, may degrade the performance of the classification model. Often these changes make the model inconsistent and regular updatings become necessary. Taking the temporal dimension into account during the analysis of Web usage data is a necessity, since the way a site is visited may indeed evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. One solution to this problem, proposed in this article, is to update models using summaries obtained by means of an evolutionary approach based on an intelligent clustering approach. We carry out various clustering strategies that are applied on time sub-periods. To validate our approach we apply two external evaluation criteria which compare different partitions from the same data set. Our experiments show that the proposed approach is efficient to detect the occurrence of changes.

Functional data analysis involves data described by regular functions rather than by a finite number of real valued variables. While some robust data analysis methods can be applied directly to the very high dimensional vectors obtained from a fine grid sampling of functional data, all methods benefit from a prior simplification of the functions that reduces the redundancy induced by the regularity. In this paper we propose to use a clustering approach that targets variables rather than individual to design a piecewise constant representation of a set of functions. The contiguity constraint induced by the functional nature of the variables allows a polynomial complexity algorithm to give the optimal solution.

Metric learning makes it plausible to learn distances for complex distributions of data from labeled data. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well. Those that learn multiple metrics throughout the space have demonstrated superior accuracy, but at the cost of computational efficiency. Here, we take a new angle to the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space. This metric adaptation is accomplished by using a random forest-based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation. We have implemented and tested our method against state of the art global and multi-metric methods on a variety of data sets. Overall, the proposed method outperforms both types of methods in terms of accuracy (consistently ranked first) and is an order of magnitude faster than state of the art multi-metric methods (16x faster in the worst case).

In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows' $C_p$ type criteria may be used as a tuning parameter selection tool in lasso-type regularization methods, for which the concept of degrees of freedom plays a key role. In the present paper, we propose an efficient algorithm that computes the degrees of freedom by extending the generalized path seeking algorithm. Our procedure allows us to construct model selection criteria for evaluating models estimated by regularization with a wide variety of convex and non-convex penalties. Monte Carlo simulations demonstrate that our methodology performs well in various situations. A real data example is also given to illustrate our procedure.