@conference {17630,
	title = {Networking lessons: From computers to water},
	booktitle = {2012 Fourth International Conference on Communication Systems and Networks (COMSNETS)},
	year = {2012},
	month = {2012/01/03/7},
	pages = {1 - 6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As an instance of using IT to green non-IT domains, we consider the question whether lessons from computer networking can be applied in water distribution networks to improve their energy footprint and/or efficiency. Our contributions in this work are: (i) we identify several areas where principles from computer networking can be used to better water distribution; (ii) we focus on a specific infrastructure enhancement problem caused by increasing demands on a water utility network and present solutions (similar to those used in computer networks) that optimize both operational expenditure and total cost of ownership. We validate our solutions through simulations and compare their efficacy against techniques that are traditionally used in enhancing water networks. Our results show that lessons from computer networks can help in enhancing water networks.},
	keywords = {Biological cells, computer networking, Computer networks, COMPUTERS, energy footprint, Genetic algorithms, green nonIT domains, infrastructure enhancement problem, Internet, network theory (graphs), networking lessons, planning, pricing, water distribution networks, water supply, water utility network},
	isbn = {978-1-4673-0296-8},
	doi = {10.1109/COMSNETS.2012.6151373},
	author = {Narayanan,I. and Sarangan,V. and Vasan, A. and Srinivasan, Aravind and Sivasubramaniam,A.}
}
@article {18908,
	title = {Inferencing in Support of Active Templates},
	year = {2005},
	month = {2005/02//},
	institution = {Department of Computer Science, University of Maryland, College Park},
	abstract = {The primary accomplishments of this project include the following: (1) HICAP is a general purpose planning architecture to assist military commanders with planning NEOs (Noncombatant Evacuation Operations). HICAP integrates a hierarchical task editor with a planning tool. Military planners select a task to decompose in the task editor and then use the planning tool to interactively refine it into an operational plan. (2) SHOP and SHOP2 are some simple, practical planning tools based on HTN planning. Their practical utility is shown by the mergence of an active set of users, which include government laboratories, industrial R\&D projects, and academic settings. SHOP2 received one of the top four awards in the 2002 International Planning Competition. (3) The Air Force Research Laboratory{\textquoteright}s Causal Analysis Tool (CAT) is a system for creating and analyzing causal models similar to Bayesian networks. We have enhanced CAT by developing an approach to quickly generate plans that have high probabilities of success.},
	keywords = {*ARTILLERY AMMUNITION, *HIGH CAPACITY PROJECTILES, AMMUNITION AND EXPLOSIVES, BAYES THEOREM, COALITION, Collaboration, Computer architecture, COMPUTER HARDWARE, Computer networks, HICAP(HIGH CAPACITY ARTILLARY PROJECTILES), MILITARY COMMANDERS, NEO(NONCOMBATANT EVACUATION OPERATIONS), NONCOMBATANT., PE63760E, planning, probability, PROJECTILE TRAJECTORIES, TASK SUPPORT, WUAFRLATEMPO02},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431023},
	author = {Nau, Dana S.}
}
@conference {11960,
	title = {Purposive and qualitative active vision},
	booktitle = {Proceedings of 10th International Conference on Pattern Recognition, 1990},
	volume = {i},
	year = {1990},
	month = {1990/06/16/21},
	pages = {346-360 vol.1 - 346-360 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The traditional view of the problem of computer vision as a recovery problem is questioned, and the paradigm of purposive-qualitative vision is offered as an alternative. This paradigm considers vision as a general recognition problem (recognition of objects, patterns or situations). To demonstrate the usefulness of the framework, the design of the Medusa of CVL is described. It is noted that this machine can perform complex visual tasks without reconstructing the world. If it is provided with intentions, knowledge of the environment, and planning capabilities, it can perform highly sophisticated navigational tasks. It is explained why the traditional structure from motion problem cannot be solved in some cases and why there is reason to be pessimistic about the optimal performance of a structure from motion module. New directions for future research on this problem in the recovery paradigm, e.g., research on stability or robustness, are suggested},
	keywords = {active vision, Automation, brain models, complex visual tasks, Computer vision, environmental knowledge, highly sophisticated navigational tasks, HUMANS, Image reconstruction, intentions, Kinetic theory, Laboratories, Medusa, Motion analysis, Navigation, planning, planning (artificial intelligence), purposive-qualitative vision, recovery problem, Robust stability, Robustness, SHAPE, stability},
	isbn = {0-8186-2062-5},
	doi = {10.1109/ICPR.1990.118128},
	author = {Aloimonos, J.}
}
@article {15671,
	title = {On Finding Shortest Paths on Convex Polyhedra.},
	year = {1985},
	month = {1985/05//},
	institution = {CENTER FOR AUTOMATION RESEARCH, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Applications in robotics and autonomous navigation have motivated the study of motion planning and obstacle avoidance algorithms. The special case considered here is that of moving a point (the object) along the surface of a convex polyhedron (the obstacle) with n vertices. Sharir and Schorr have developed an algorithm that, given a source point on the surface of a convex polyhedron, determines the shortest path from the source to any point on the polyhedron in linear time after O(n cubed log n) preprocessing time. The preprocessed output requires O(n squared) space. By using known algorithms for fast planar point location, the shortest path query time for Sharir and Schorr{\textquoteright}s algorithm is shown to be O(k + log n) where k is the number faces traversed by the path. We give an improved preprocessing algorithm that runs in O(n squared log n) time requiring the same query time and space. We also show how to store the output of the preprocessing algorithm in O(n log n) space while maintaining the same query time. (Author)},
	keywords = {*ALGORITHMS, *POLYGONS, *Polyhedrons, *PREPROCESSING, AUTONOMOUS NAVIGATION, AVOIDANCE., BARRIERS, INTERROGATION, MOTION, output, PATHS, PE61102F, planning, POSITION(LOCATION), ROBOTICS, THEORETICAL MATHEMATICS, TIME},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA166246},
	author = {Mount, Dave}
}
