Each student will be presenting one of the papers indicated below.

I - Reasoning Under Uncertainty

1. Alan Liu et. al; Customizing Directions in an Automated Wayfinding System for Individuals with Cognitive Impairment; Proceedings of ASSETS 2009. http://portal.acm.org/citation.cfm?id=1639649
2. S. Intille et al.; Just-In-Time Context Sensitive Questioning for Preventative Health Care; Proceedings of AAAI Workshop on Automation as Caregiver; 2002. http://web.media.mit.edu/%7Eintille/papers-files/AAAIElder02.pdf
3. T. Zeng; Mixture classification model based on clinical markers for breast cancer prognosis; Artificial Intelligence in Medicine; Vol 48 Issue 2-3; 2010. 6745416197516418909.pdf
4. L. Nunes; Markov Decision Process Applied to the control of hospital elective admissions; Artificial Intelligence in Medicine Vol 47 Iss 2009. 10460545930909001261.pdf
5. P. Demeester et al.; A Hybrid Tabu Search Algorithm for Automatically Assigning Patients to Beds; Artificial Intelligence in Medicine; Vol 48 Issue 1, 2010. 7283770962056173435.pdf

II - Natural Language Processing

1. N. Green and B. Davis; Dialogue Generation in an Assistive Skills Training System for Caregivers of Persons with Alzheimer's Disease; AAAI Spring Symposium Series; 1993. http://www.aaai.org/Papers/Symposia/Spring/2003/SS-03-06/SS03-06-007.pdf
2. G. Ferguson et al; Cardiac: An Intelligent Conversational Assistant for Chronic Heart Failure Patient Health Monitoring; AAAI Fall Symposium on Virtual Healthcare Interaction; 2009. http://www.aaai.org/ocs/index.php/FSS/FSS09/paper/viewFile/915/1175
3. N. Green; A Bayesian Network Coding Scheme for Annotating Biomedical Information Presented to Genetic Counselling Clients; Journal of Biomedical Informatics Vol 38 Iss 2; 2005. http://ils.unc.edu/bmh/tmp/NancyGreen-in-press.pdf
4. A. Cesta et al.; Supporting Interactionin the RoboCare Intelligent Assistive Environment; AAAI Spring Symposium on Intelligent Interaction 2007; www.ai.sri.com/~nysmith/organizing/sss07/papers/SS04CestaA.pdf
5. A. Gatt et al.; From Data to Text in the Neonatal Intensive Care Unit: Using NLG Technology for Decision Support and Information Management; AI Communications 22: 153-186; 2009; http://www.csd.abdn.ac.uk/~ereiter/papers/aicomm09.pdf

III - Intelligent Tutoring

1. R. Crowley and O. Meldeveda; An intelligent tutoring system for visual classification problem solving; Artificial Intelligence in Medicine Vol.6 Issue 1; 2006. 7180000912503924842.pdf
2. G. el Saadawi et al.; Factors Affecting Feeling-of-Knowing in a Medical Intelligent Tutoring System: The Role of Immediate Feedback as a Metacognitive Scaffold; Advances in Health Sciences Education (2010) 15:9-30; 2010. http://www.springerlink.com/content/p5r248465417t84r/fulltext.pdf
3. V. Payne; Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths; Artificial Intelligence in Medicine; Vol 47 175-197; 2009. 13637241126869170533.pdf
4. P. Haddawy; COMET: A Collaborative Tutoring System for Medical Problem-Based Learning; IEEE Intelligent Systems; Vol 22 Iss 7l 2007. http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=4287276

IV - Multiagent Systems

1. A. Moreno et al.; A Multi-Agent System to Schedule Organ Transplant Operations; Inteligentcia Artificial No. 13 (2001); redalyc.uaemex.mx/pdf/925/92521305.pdf
2. D. Abraham; Clearing Algorithms for Barter Exchange Markets: Enabling Nationwide Kidney Exchanges; http://www.cs.uwaterloo.ca/~klarson/teaching/F08-886/papers/Abraham07Kidney.pdf
3. K. Decker and J. Li; Coordinated Hospital Patient Scheduling; Proceedings of ICMAS 1998; www.cmpe.boun.edu.tr/courses/cmpe593/spring2008/reading/icmas98.ps

V - Computer Vision

1. S. Ng et al.; Towards a Mobility Diagnostic Tool: Tracking Rollator Users' Leg Pose With a Monocular Vision System; http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=5333085
2. A. Mihailidis et al; The use of computer vision in an intelligent environment to support aging-in-place, safety and independence at home; Proceedings of IEEE Engineering Medicine and Biology; 2009. http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=01331400
3. D. Klimov; Intelligent visualization and exploration of time-oriented data of multiple patients; Artificial Intelligence in Medicine; 2010. 5235880526386162235.pdf
4. J. Hoey; Tracking Using Flocks of Features, with Application to Assisted Handwashing; British Machine Vision Conference; 2006. http://www.computing.dundee.ac.uk/staff/jessehoey/papers/hoeybmvc06.pdf
5. S. Bahadori et al.; Towards Ambient Intelligence for the Domestic Care of the Elderly; The RoboCare Technical Reports RC-TR-0704-3; 2000. www.dis.uniroma1.it/~leone/mypapers/rc-tr-0704-3.pdf

VI - User Modeling

1. F. de Rosis et al.; User Modeling and Adaptation in Health Promotion Dialogs with an Animated Character; Journal of Medical Bioinformatics, Volume 39 Issue 5; October 2006. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.4085&rep=rep1&type=pdf
2. C. Geib; Problems with Intent Recognition for Elder Care; AAAI Workshop on Automation as Caregiver, 2002. http://homepages.inf.ed.ac.uk/cgeib/Site/Papers_files/eldersGeib.pdf
3. N. Elhadad et al.; Customization in a unified framework for summarizing medical literature; Artificial Intelligence in Medicine 33(2), 179-198; 2005. citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.3497
4. N. Baghaei et al; SOFA: An Online Social Network for Engaging and Motivation Families to Adopt a Healthy Lifestyle; Proceedings of Australian HCI Conference; 2009. http://delivery.acm.org/10.1145/1740000/1738871/p269-baghaei.pdf?key1=1738871&key2=0711973721&coll=GUIDE&dl=GUIDE&CFID=88268036&CFTOKEN=23617283
5. N. Green et al.; User Modeling for Tailored Genomic e-Health Information; Proceedings of User Modeling 2005 workshop on e-Health; 2005; www.csc.liv.ac.uk/~floriana/UM05-eHealth/Green.pdf