State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo. We also present stochastic variational inference and online learning approaches for fast learning with long time series.

This paper presents an interactive narrative framework we have designed for games that promote health behavior change. The framework aims to address two key issues: player engagement with the game, and player adherence to the health behavior change-related homework they receive in the game. In this paper, we describe our narrative system that tackles these issues and a prototype game that promotes physical activity in which our narrative system is integrated.

AI research in interactive narrative often lacks specificity as to the player experience it is trying to enable. In this paper, we consider a set of desirable elements from narrative and interactive experiences, and show by looking at playable experiences from industry and academia that combining them has the potential to be limited or self-defeating. To address these issues, we propose opportunistic storytelling , a set of design principles for near-term playable interactive narratives.

Adaptation is a critical component of collaboration. Nevertheless, online learning is not yet used in most successful human-robot interactions, especially when the human's and robot's goals are not fully aligned. There are at least two barriers to the successful application of online learning in HRI. First, typical machine-learning algorithms do not learn at time scales that support effective interactions with people. Algorithms that learn at sufficiently fast time scales often produce myopic strategies that do not lead to good long-term collaborations. Second, random exploration, a core component of most online-learning algorithms, can be problematic for developing collaborative relationships with a human partner. We anticipate that a new genre of online-learning algorithms can overcome these two barriers when paired with (cheap-talk) communication. In this paper, we overview our efforts in these two areas to produce a situation-independent, learning system that quickly learns to collaborate with a human partner.

This paper discusses how AI models of argumentation can play a role in personalized and participatory medicine. It describes our previous research on natural language generation of argumentation for genetic counseling and a pilot study on risk visualization, and our current research on argumentation mining.

The problem of transferring skills to hyper-redundant system requires the design of new motion primitive representations that can cope with multiple sources of noise and redundancy, and that can dynamically handle perturbations in the environment. One way is to take inspiration from invertebrate systems in nature to seek for new versatile representations of motion/behavior primitives for continuum robots. In particular, the incredibly varied skills achieved by the octopus can guide us toward the design of such robust encoding scheme. This abstract presents our ongoing work that aims at combining statistical machine learning, dynamical systems and stochastic optimization to study the problem of transferring skills to a flexible surgical robot (STIFF-FLOP) composed of 2 modules with constant curvatures. The approach is tested in simulation by imitation and self-refinement of an octopus reaching motion.

In this summary report I give an account of research conducted over the last two years, showing the suitability and the advantages of applying computational analogy-engines in the analysis and design of analogy-based methods and tools in teaching and education. This overview constitutes the conclusion of the first phase of a multi-stage effort trying to introduce computational models of analogy also to education and the learning sciences, thus opening up these fields to computational tools and methods not only on an instrumental level, but also in analytical, conceptual, and design-oriented studies. I locate the "analogy-engines in the classroom" research program within the bigger schemes of studying human creativity and computational creativity, provide an introduction to the theoretical underpinnings of the endeavor, and revisit three worked out case studies serving as proofs of the feasibility of the overall approach.

The two applications under development The EMOTE project (http://www.emote-project.eu/) is are a Treasure Hunt exercise designed to teach mapreading working towards the development of an empathic robot tutor skills, and a multi-player game Enercities-2 designed to be used with the 11-14 group and a multi-touch table to teach aspects of sustainable urban development.

Computer Science researchers have long sought ways to apply the fruits of their labors to education. From the Logo turtles to the latest Cognitive Tutors, the allure of computers that will understand and help humans learn and grow has been a constant thread in Artificial Intelligence research. Now, advances in robotics and our understanding of Human-Robot Interaction make it feasible to develop physically-present robots that are capable of presenting educational material in an engaging manner, adapting online to sensory information from individual students, and building sophisticated, personalized models of a student’s mastery over complex educational domains. In this paper, we discuss how using physical robots as platforms for artificially intelligent tutors enables an expanded space of possible educational interactions. We also describe a work-in-progress to (1) extend previous work in personalized user models for robotic tutoring and (2) further explore the differences between interaction with physical robots and onscreen agents. Specifically, we are examining how embedding an tutoring interaction inside a story, game, or activity with an agent may differentially affect learning gains and engagement in interactions with physical robots and screen-based agents.

We address two domains of skill transfer problems encountered by an autonomous robot: within-domain adaptation and cross-domain transfer. Our aim is to provide skill representations which enable transfer in each problem classification. As such, we explore two approaches to skill representation which address each problem classification separately. The first representation, based on mimicking, encodes the full demonstration and is well suited for within-domain adaptation. The second representation is based on imitation and serves to encode a set of key points along the trajectory, which represent the goal points most relevant to the successful completion of the skill. This representation enables both within-domain and cross-domain transfer. A planner is then applied to these constraints, generating a domain-specific trajectory which addresses the transfer task.