The challenges of effective health risk communication are well known. This paper provides pointers to the health communication literature that discuss these problems. Tailoring printed information, visual displays, and interactive multimedia have been proposed in the health communication literature as promising approaches. On-line risk communication applications are increasing on the internet. However, potential effectiveness of applications using conventional computer technology is limited. We propose that use of artificial intelligence, building upon research in Intelligent Tutoring Systems, might be able to overcome these limitations.

This article presents our work on building a virtual coach agent, called Dr. Vicky, and training environment (called the Virtual BNI Trainer, or VBT) for learning how to correctly talk with medical patients who have substance abuse issues. This work focuses on how to effectively design menu-based dialogue interactions for conversing with a virtual patient within the context of learning how to properly engage in such conversations according to the brief negotiated interview techniques we desire to train. Dr. Vicky also employs a model of student knowledge to influence the mediation strategies used in personalizing the training experience and guidance offered. The VBT is a prototype training application that will be used by medical students and practitioners within the Yale medical community in the future.

The past decade has witnessed an increasing interest in the use of virtual coaches in healthcare. This paper describes a virtual coach to provide mindfulness meditation training, and the coaching support necessary to begin a regular practice. The coach is implemented as an embodied conversational character, and provides mindfulness training and coaching support via a web-based application. The coach is represented as a female character, capable of showing a variety of affective and conversational expressions, and interacts with the user via a mixed-initiative, text-based, natural language dialogue. The coach adapts both its facial expressions and the dialogue content to the user’s learning needs and motivational state. Findings from a pilot evaluation study indicate that the coach-based training is more effective in helping users establish a regular practice than self-administered training via written and audio materials. The paper concludes with an analysis of the coach features that contribute to these results, discussion of key challenges in affect-adaptive coaching, and plans for future work.

There is an urgent need for interventions to assist teenagers and young adults in appreciating the physical and social risks of binge drinking. While research on the health risks associated with alcohol abuse is well developed, the translation and communication of this knowledge to young people is not. This paper describes a prototype visualisation tool, an Alcohol Risk Calculator, that provides personalised information on risks associated with alcohol consumption based on individual drinking habits. Its design is informed by studies of graphical literacy, evidence on forms of presenting risk that aid understanding, and theory that provides insight into changing health damaging behaviour.

One challenge of building and maintaining large-scale data management systems is managing data fusion from multiple data sources. Often times, different data sources may represent the same data element in a slightly different way. These differences may represent an error in the data or a disagreement between sources on the correct value that best represents the data point. When the quantity of data managed and fused becomes sufficiently large, manual review becomes impossible, and automated systems must be built to manage data fusion. Some of the traditional solutions use simple voting theory, Dempster-Shafer theory, fuzzy matching and incremental learning. This paper presents a novel approach to data fusion in the domain of business listings. The task at hand, business listing categorization, suffers from conflicting and incomplete data from disparate data sources. Given the need for a high degree of accuracy in this task, we use a combination of case-based reasoning, joint probability, and domain-specific rules to improve data accuracy above other methods.

We present an approach to map Robocup-soccer narratives (in natural language) to a sequence of meaningful events. Our approach takes advantage of an action-centered framework, an inference subroutine, and an iterative learning algorithm. Our framework represents the narrative as a sequence of sentences and each sentence as a probability distribution over deterministic events. Our learning algorithm maps sentences to meaningful events without any annotated labeled data. Instead, it uses a prior knowledge about event descriptions and an inference subroutine to estimate initial training labels. The algorithm further improves the training labels at next iterations. In our experiments we demonstrate that with no labeled data our algorithm achieves higher accuracy compared to the state of the art that uses labeled data.

This paper presents the formalization of a biological process, cell division, in modular action language ALM. We show how the features of ALM — modularity, separation between an uninterpreted theory and its interpretation — lead to a simple and elegant solution that can be used in answering questions from biology textbooks.

This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning significantly improve both learning time and policy performance. Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studies the impact of demonstration quality and quantity. Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration.

As computational agents are increasingly used beyond research labs, their success will depend on their ability to learn new skills and adapt to their dynamic, complex environments. If human users — without programming skills — can transfer their task knowledge to the agents, learning rates can increase dramatically, reducing costly trials. The TAMER framework guides the design of agents whose behavior can be shaped through signals of approval and disapproval, a natural form of human feedback. Whereas early work on TAMER assumed that the agent's only feedback was from the human teacher, this paper considers the scenario of an agent within a Markov decision process (MDP), receiving and simultaneously learning from both MDP reward and human reinforcement signals. Preserving MDP reward as the determinant of optimal behavior, we test two methods of combining human reinforcement and MDP reward and analyze their respective performances. Both methods create a predictive model, H-hat, of human reinforcement and use that model in different ways to augment a reinforcement learning (RL) algorithm. We additionally introduce a technique for appropriately determining the magnitude of the model's influence on the RL algorithm throughout time and the state space.

Processing high-density clinical signal data (data from biomedical sensors deployed in the clinical environment) is resource intensive and time consuming. We propose a novel approach to storing and processing clinical signal data based on the Apache HBase distributed column-store and the MapReduce programming paradigm with an integrated web-based data visualization layer. An integrated solution negates the need to marshal data into and out of the storage system while also easily parallelizing the computation, a problem that is becoming more and more important due to increasing numbers of sensors and resulting data. We estimate upwards of 50TB of clinical signal data for a 200-bed medical center within the next 5 years. Consequently, efficient processing of clinical signal data is a vital step towards multivariate analysis of the signal data in order to develop better ways of describing a patient’s clinical status.