AAAI-15 / IAAI-15 Conference Program January 25 – 30, 2015 Hyatt Regency Austin, Austin, Texas, USA
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Using Game Theory for Los Angeles Airport Security
Articles Using Game Theory for Los Angeles Airport Security James Pita, Manish Jain, Fernando Ordóñez, Christopher Portway, Milind Tambe, Craig Western, Praveen Paruchuri, and Sarit Kraus n Security at major locations of economic or political impor tance is a key concern around P the world, particularly given the threat of ter- rotecting national infrastructure such as airports, historical rorism. Limited security resources prevent full landmarks, or a location of political or economic importance is security coverage at all times, which allows a challenging task for police and security agencies around the adversaries to observe and exploit patterns in world, a challenge that is exacerbated by the threat of terrorism. selective patrolling or mon itoring; for exam- Such protection of important locations includes tasks such as ple, they can plan an attack avoiding existing monitoring all entrances or inbound roads and checking pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization inbound traffic. However, limited resources imply that it is typ- must provide distinct weights to dif ferent ically impossible to provide full security cover age at all times. actions based on their complex costs and Furthermore, adversaries can observe se curity arrangements benefits. To this end, this article describes a over time and exploit any predictable patterns to their advan- promising transition of the lat est in multia- tage. Randomizing schedules for pa trolling, checking, or moni- gent algorithms into a deployed application. toring is thus an important tool in the police arsenal to avoid In particular, it describes a software assistant the vulnerability that comes with predictability. -
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery Peter Schulam Fredrick Wigley Suchi Saria Department of Computer Science Division of Rheumatology Department of Computer Science Johns Hopkins University Johns Hopkins School of Medicine Department of Health Policy & Mgmt. 3400 N. Charles St. 733. N. Broadway Johns Hopkins University Baltimore, MD 21218 Baltimore, MD 21205 3400 N. Charles St. [email protected] [email protected] Baltimore, MD 21218 [email protected] Abstract more thorough retrospective or prospective study to confirm their existence (e.g. Barr et al. 1999). Recently, however, Diseases such as autism, cardiovascular disease, and the au- literature in the medical community has noted the need for toimmune disorders are difficult to treat because of the re- markable degree of variation among affected individuals. more objective methods for discovering subtypes (De Keu- Subtyping research seeks to refine the definition of such com- lenaer and Brutsaert 2009). Growing repositories of health plex, multi-organ diseases by identifying homogeneous pa- data stored in electronic health record (EHR) databases tient subgroups. In this paper, we propose the Probabilis- and patient registries (Blumenthal 2009; Shea and Hripc- tic Subtyping Model (PSM) to identify subgroups based on sak 2010) present an exciting opportunity to identify disease clustering individual clinical severity markers. This task is subtypes in an objective, data-driven manner using tools challenging due to the presence of nuisance variability— from machine learning that can help to tackle the problem variations in measurements that are not due to disease of combing through these massive databases. -
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -