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.

An appealing alternative to tediously specifying robot behaviors in response to particular image features is to have the robot’s behavior be induced by human decisions made when piloting the robot. This paper presents one promising approach to creating this alternative. A human pilots a camera-equipped robot, which builds a representation of its target environment using Growing Neural Gas (GNG). The robot associates an action with each GNG node based on what the human pilot was doing while the node was active. When running autonomously, the robot chooses the action associated with the node that is the closest match to the current input image. Preliminary results suggest that the approach has potential, but that subsequent alteration of the actions induced for some of the GNG nodes is important for acceptable performance.

We can also leverage medical ontologies/taxonomies to help Traditionally, artificial intelligence in medical applications abstract specific details to concepts that can be more easily has focused on improving the abilities of medical professionals introduced and then later refined when a patient is ready. Additionally, to perform tasks such as diagnosis (e.g., Shortliffe we can have annotations to provide information 1986; Wyatt and Spiegelhalter 1991; Garg et al. 2005; Vihinen about the authoritativeness of content. Furthermore, in many and Samarghitean 2008) or to aid in managing drug interactions cases information will need to travel beyond the patient to (e.g., Bindoff et al. 2007) or side effects (Edwards family or hired caregivers (Williams et al. 2002, p. 387), and Aronson 2000, p. 1258). These efforts target users who which means that multiple explanations will need to be generated have years of medical experience. In contrast, patients often based on the target individual's knowledge. Explanation have limited medical knowledge, and they may be coping generation also involves applications of user modeling with new life-threatening diagnoses that may require a number (e.g.

A "meta-study" or "meta-analysis" analyzes multiple medical studies related to the same disease, treatment protocol, and outcome measurement to identify if there is an overall effect or not (e.g., treatment induces remission or causes adverse effects). It's advantage lies in the pooling and analysis of results across independent studies, which increases the population size, mitigates some experimental bias or inconsistent results from a single study, etc. Meta-studies are important for understanding the effectiveness (or not) of treatment, influencing clinical guidelines and for spurring new research directions. However, meta-studies are extremely time consuming to construct by hand and keep updated with the latest results. This limits both their breadth of coverage (since researchers will only invest the time for diseases they are interested in) and their practically. Yet, high-quality medical research is increasing at a staggering rate, and there is an opportunity to apply automation to this increasing body of knowledge, thereby expanding the benefits of meta-studies to (theoretically) all diseases and treatment, as they are published. That is, we envision, long term an automatic process for creating meta-studies across all diseases and treatments, and keeping those meta-studies up-to-date automatically. In this paper we demonstrate that there is potential to perform this task, point out future research directions to make this so, and, hopefully, spur significant interest in this compelling and important research direction at the intersection of medical research and machine learning.

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.

A paradigm shift is taking place in the field of men- tal healthcare and patient wellbeing. Traditionally, the attempts at sustaining and enhancing wellbeing were mainly based on the comparison of the individual with the population average. Recently, attention has shifted towards a more personal, idiographic approach. Such shift calls for new solutions to get data about individu- als, create personalized models of wellbeing and trans- lating these into personalized advice. Idiographic research can be conducted on a large scale by letting people measure themselves. Repeated collec- tion of data, for example by means of questionnaires, provides individuals feedback on and insight into their wellbeing. A way to partially automate this feedback process is by creating software that statistically ana- lyzes, using a method known as vector autoregression, repetitive questionnaire data to determine cause-effect relationships between the measured features. In this pa- per we describe a means to facilitate these repetitive measurements and to partially automate the feedback process. The paper provides an overview and technical description of such automated analyses software, named Autovar, and its use in an online self-measurement plat- form.

In this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to estimate missing variables for non-adopters, so that attributes of non-adopters and adopters could be used to train a logistic regression model. Then, we integrated the logistic regression model along with other predictive models into a multi-agent simulation platform and validated our models by comparing the forecast of aggregate adoptions in a typical zip code area with its ground truth. This result shows that the agent-based model can reliably predict future adoptions. Finally, based on the validated agent-based model, we compared the outcome of a hypothesized seeding policy with the original incentive plan, and investigated other alternative seeding policies which could lead to more adopters.

Behavioral scientists contend that individuals, and organizations rarely make decisions solely on the basis of economic factors. Decisions are also shaped by perceived risk, social interactions, currency and salience of information, and other value propositions. Social diffusion of information on consumer experiences, entrance of new business models better aligned with customers’ concerns when evaluating investments, and perceived improving economic conditions are all factors in consumers’ decisions to adopt a new technology, such as solar photovoltaics (PV). We describe a new conceptual agent-based model, BE-Solar, that incorporates a social and behavioral decision framework for technology adoption decisions. We demonstrate the feasibility of including heterogeneity and behavioral factors into an agent-based model of the solar PV market, which is being applied to the Southern California market.

An important contribution of our work is to quantitatively The SunShot Initiative (Sunshot 2011) has the goal of reducing assess the impact of peer effects on PV adoption in relationship the total costs for photovoltaic (PV) solar energy systems to other economic and non-economic variables. It has to be "cost-competitive" with other forms of energy. At that long been noted that peer effects play a significant role in cost, PV could be widely adopted and thus allow the United the adoption of new technology. For instance, (Rogers 2003) States (US) increase it's use of clean energy - a goal of the highlights the importance of "opinion leaders" and interpersonal Department of Energy (U.S.

Using geospatial methods, this paper informs the evolving field of research on the diffusion of residential Third Party Owned PV systems by analyzing 1) the spatial distribution of TPO systems, and 2) the influence of demographics on the adoption on the local level. This research is part of a multidisciplinary study into the diffusion of solar technology (SEEDS), using San Diego County as focus area. Our findings reveal a significant clustering of TPO PV adoption in San Diego County. TPO systems reached a similarly high market share across a large area in the central county in contrast to the installation of host-owned systems, which have been less evenly distributed across single-family households in the same area. The diffusion of TPO systems in San Diego County can be partially explained by looking at median income and percentage of people born in the US. The explanatory power of the model varies across the region.