Children with complex health conditions require care from a large, diverse set of caregivers that includes parents and community support organizations as well as multiple types of medical professionals. Coordination of their care is essential for good outcomes, and  extensive  research has shown that the use of integrated, team-based care plans improves care coordination. Care plans, however, are rarely deployed in practice. This paper describes barriers to effective implementation of care plans in complex care revealed by a study of care providers treating such children. It draws on teamwork theories, identifying ways AI capabilities could enhance care plan use; describes the design of GoalKeeper, a system to support providers use of care plans; and describes  initial work toward information sharing algorithms for such systems.

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.

The tight long-run fit of the learning curve has led to its use as a tool to predict the future cost of solar panels. Nemet (2006) is skeptical of the view that learning has been an important driver of cost reduction, and uses data during 1975-2002 to show that increases in plant size has been the most important driver of reduction in cost per watt.

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.

Subnational policies pertaining to photovoltaic (PV) systems have increased in volume in recent years and federal incentives are set to be phased out over the next few. Understanding how subnational policies function within and across jurisdictions, thereby impacting PV market development, informs policy decision making. This report was developed for subnational policy-makers and researchers in order to aid the analysis on the function of PV system incentives within the emerging PV deployment market. The analysis presented is based on a ‘logic engine’, a database tool using existing state, utility, and local incentives allowing users to see the interrelationships between PV system incentives and parameters, such as geographic location, technology specifications, and financial factors. Depending on how it is queried, the database can yield insights into which combinations of incentives are available and most advantageous to the PV system owner or developer under particular circumstances. This is useful both for individual system developers to identify the most advantageous incentive packages that they qualify for as well as for researchers and policymakers to better understand the patch work of incentives nationwide as well as how they drive the market. In the case of the latter, findings from initial queries identify a limited connection between incentives and market development (based on current data) and point to differing complexities for system developers depending on system owner and size. The entire effort reveals (or possibly reiterates) a critical lack of data on both local policy environments and the structure of market penetration to be able to understand the impact of subnational incentives on the market.

AI and robotics researchers aim at having robots in our environments coexisting with humans, as artificial creatures that will help humans and collaborate with humans to improve our societies. There will be more than one robot. Robots will not interact with some humans just once, or a few times, but many times. Humans will interact with robots to change their requests and to teach and correct their behaviors. This abstract briefly discusses a few issues for AI and HRI for such challenging repeated interactions in space and time between robots and humans. We have made different levels of research progress on these issues, as we discuss. Our presentation is motivated by our work with the CoBot mobile service robots, which have been running in our environments for the last three years, and for more than 500kms.

A robotic system for extracting and imitating gestures in Human-Robot Interaction is presented. Using the iCub robot and RGB-D sensors, contextually important time points are used to mark and ground the gestures. The benefits of this approach are that the gesture imitation appears both visually and behaviourally realistic, the cognitive and computation loads are reduced and no predefined mappings or speech-based annotations are needed. The system is adaptive to individual users and contributes to the creation and maintenance of a smoother interaction.

Progress in general HRI metrics, while significant, is often in the form of post-experiment questionnaires or expert video analysis. This is a significant hurdle for any intelligent system that aspires to interact with its social environment. In the case of social navigation, robots must react to a dynamic environment. Socially aware robot behavior requires real time quantitative metrics of human subjective experience. This is a vast topic, which we approach by trying to measure how predictable robot actions are from its impact on the paths taken by passers-by. We chose predictability as a first metric because it can be modelled in terms of efficiency, and it affects safety. Thus, it serves as an interesting proof of concept.