The lab of Atul Butte builds and applies computational tools that convert more than 300 billion points of molecular, clinical, and epidemiological data-measured by researchers and clinicians over the past decade-into new diagnostics, therapeutics, and novel insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his team's recent work on clinical evaluations of patients presenting with personal genomes, enabled by the largest curated database of human disease-associated SNP's. With so many genomes now sequenced from individuals from a variety of ethnic backgrounds, and analyzed in a clinical context, Dr. Butte will present how ethnicity alters the background distribution of disease SNP's. Finally, Dr. Butte will also present his team's recent work on environment-wide association studies (EWAS) and how they enable studies of gene-environment interactions.

Currently, the majority of matchers are able to establish simple correspondences between entities, but are not able to provide complex alignments. Furthermore, the resulting alignments do not contain additional information on how they were extracted and formed. Not only it becomes hard to debug the alignment results, but it is also difficult to justify correspondences. We propose a method to generate complex ontology alignments that captures the semantics of matching algorithms and human-oriented ontology alignment definition processes. Through these semantics, arguments that provide an abstraction over the specificities of the alignment process are generated and used by agents to share, negotiate and combine correspondences. After the negotiation process, the resulting arguments and their relations can be visualized by humans in order to debug and understand the given correspondences.

While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11.

This paper takes a new approach to the old adage that knowledge is the key for artificial intelligence. A cognizant robot is a robot with a deep and immediately accessible understanding of its interaction with the environment — an understanding the robot can use to flexibly adapt to novel situations. Such a robot will need a vast amount of situated, revisable, and expressive knowledge to display flexible intelligent behaviors. Instead of relying on human-provided knowledge, we propose that an arbitrary robot can autonomously acquire pertinent knowledge directly from everyday interaction with the environment. We show how existing ideas in reinforcement learning can enable a robot to maintain and improve its knowledge. The robot performs a continual learning process that scales-up knowledge acquisition to cover a large number of facts, skills and predictions. This knowledge has semantics that are grounded in sensorimotor experience. We see the approach of developing more cognizant robots as a necessary key step towards broadly competent robots.

The paper describes ongoing integrated research on designing intelligent robots that can assist humans in making a situation assessment during Urban Search & Rescue (USAR) missions. These robots (rover, microcopter) are deployed during the early phases of an emergency response. The aim is to explore those areas of the disaster hotzone which are too dangerous or too difficult for a human to enter at that point. This requires the robots to be "intelligent" in the sense of being capable of various degrees of autonomy in acting and perceiving in the environment. At the same time, their intelligence needs to go beyond mere task-work. Robots and humans are interdependent. Human operators are dependent on these robots to provide information for a situation assessment. And robots are dependent on humans to help them operate (shared control) and perceive (shared assessment) in what are typically highly dynamic, largely unknown environments. Robots and humans need to form a team. The paper describes how various insights from robotics and Artificial Intelligence are combined, to develop new approaches for modeling human robot teaming. These approaches range from new forms of modeling situation awareness (to model distributed acting in dynamic space), human robot interaction (to model communication in teams), flexible planning (to model team coordination and joint action), and cognitive system design (to integrate different forms of functionality in a single system).

The current era of internet-facilitated bigger data, better tools, and collective intelligence community computing is accelerating advances in many areas ranging from artificial intelligence to knowledge generation to public health. In the health sector, data volumes are growing with genomic, phenotypic, microbiomic, metabolomic, self-tracking, and other data streams. Simultaneously, tools are proliferating to allow individuals and groups to make sense of these data in a participatory manner through personal health tracking devices, mobile health applications, and personal electronic medical records. Health community computing models are emerging to support individual activity and mass collaboration through health social networks and crowdsourced health research studies. Participatory health efforts portend important benefits based on both size and speed. Studies can be carried out in cohorts of thousands instead of hundreds, and it could be possible to apply findings from newly-published studies with near-immediate speed. One operator of interventional crowdsourced health research studies, DIYgenomics, has several crowdsourced health research studies in open enrollment as of January 2012 in the areas of vitamin deficiency, aging, mental performance, and epistemology. The farther future of intelligent health community computing could include personal health dashboards, continuous personal health information climates, personal virtual coaches (e.g.; Siri 2.0), and an efficient health frontier of dynamic personalized health recommendations and action-taking.

In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

In the problem of task allocation, form of probabilistic response tendencies can be used to redundancy refers to extra agents beyond the minimum achieve redundancy when an MAS is working on a problem number of required agents that have the capability to perform in which experience is beneficial. We assume that the MAS a given task. Particularly in problems where experience is a response threshold system (Bonabeau, Theraulaz, and is beneficial, redundancy provides an MAS with a Deneubourg 1998) and that previous experience on a task backup pool of ready actors if the primary actors are unavailable improves an agent's future performance on that task.

Computational models of aggregated social agents have two major faults: (1) inter-individual entrainment is ignored; and (2) rule-sets governing behavior are invariant to history. Together these shortcomings impede our ability to generate realistic models of complex evolving social processes. To illustrate how even simple couplings within an established dyad generates unexpected outcomes, we present our findings from two computer models (agent-based, particle filter) of married couples. With the use of computational modeling, especially when attempting to capture and articulate trajectories of socially aggregated agents, numerous implicit assumptions are made and yet, many if not most, are without an empirical Figure 1: User interface showing parameter sliderbars that foundation. For example, the standard protocol for creating modify interaction characteristics.

Hybrid group autonomy, organizations and teams composed of humans, machines and robots, are important to AI. Unlike the war in Iraq in 2002, the war in Afghanistan has hundreds of mobile robots aloft, on land, or under the sea. But when it comes to solving problems as part of a team, these agents are socially passive. Were the problem of aggregation and the autonomy of hybrids to be solved, robot teams could accompa- ny humans to address and solve problems together on Mars, under the sea, or in dan- gerous locations on earth (such as, fire-fighting, reactor meltdowns, and future wars). “Robot autonomy is required because one soldier cannot control several robots ... [and] because no computational system can discriminate between combatants and innocents in a close-contact encounter.” (Sharkey, 2008) Yet, today, one of the fundamental unsolved problems in the social sciences is the aggregation of individual data (such as preferences) into group (team) data (Giles, 2011) The original motivation behind game theory was to study the effect that multi- ple agents have on each other (Von Neumann and Morgenstern, 1953), known as interdependence or mutual dependence. Essentially, the challenge addresses the ques- tion: why is a group different from the collection of individuals who comprise the group? That the problem remains unsolved almost 70 years later is a remarkable com- ment on the state of the social sciences today, including game theory and economics. But solving this challenge is essential for the science and engineering of multiagent, multirobot and hybrid environments (that is, humans, machines and robots working together).