PDF-ARA Anytime A with Provable Bounds on SubOptimality Maxim Likhachev Geoff Gordon and Sebastian

cmuedu Abstract In real world planning problems time for deliberation is often limited Anytime planners are well suited for these problems they 64257nd a feasi ble solution quickly and then continually work on improving it until time runs out In this. cscmuedu thrun In Proceedings of Uncertainty in AI U AI 2002 Abstract In recent years particle 64257lters ha solv ed se eral hard perceptual problems in robotics Early successes of particle 64257lters were limited to lo wdimensional esti mation probl Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process Dynamic objects however can lead to serious errors in the re sulting maps such as spurious objects or misalignmen cmuedu Joshua Brody IIIS ITCS Tsinghua University Beijing China joshuaebrodygmailcom Kevin Matulef IIIS ITCS Tsinghua University Beijing China matulefgmailcom Abstract We develop a new technique for proving lower bounds in property testing by showing MCL is a version of Markov localization a family of probabilis tic approaches that have r ecently been applied with great practical success However previous approaches were ei ther computationally cumbersome such as gridbased ap proaches that repres We discuss the fundamental similarities between static algorithms eg A replanning algorithms eg D anytime algorithms eg ARA and anytime re planning algorithms eg AD We introduce the mo tivation behind each class of algorithms discuss their use on re ECT 1989 Carnegie Mellon University 89 10 10171 Appved for pus bc te Dtatzlbaton WUftitied brPage 2br Unclassified SECURITY CLASSIFICATION OF THIS PAGE REPORT DOCUMENTATION PAGE Ia JPOfT SECLINTY CLASSIFICATION lb RESTRICTIVE MARKINGS 2a SECURITY C scanfd val Carnegie Mellon return y Ax int matvecint A int x int y mallocNsizeofint int i j for i0 i for j0 j yi Aijxj return y brPage 5br Carnegie Mellon int p p mallocNsizeofint for i0 i pi mallocMsizeofint Carnegie Mellon int p p mallocN We present a general methodology for near optimal sensor placement in these and related problems We demonstrate that many realistic outbreak detection objectives eg de tection likelihood population a64256ected exhibit the prop erty of submodularity Assembly and Bomb Lab. 15-213: Introduction to Computer Systems . Recitation 4, Sept. 17, 2012. Outline. Assembly. Basics. Operations. Bomblab. Tools. Demo. Carnegie Mellon. Registers. Program counter. Proxylab. and stuff. 15-213: Introduction to Computer Systems. Recitation . 13: November 19, . 2012. Donald Huang (. donaldh. ). Section . M. 2. Carnegie Mellon. Topics. Summary of . malloclab. News. Machine-Level Programming II: Control. 15. -. 213: . Introduction to Computer Systems. 6. th. . Lecture,. Sep. 17, 2015. Carnegie Mellon. Instructors:. . Randal E. Bryant. and . David. R. . O’Hallaron. Stacks. 15-213: Introduction to Computer Systems. Recitation 5: September 24, 2012. Joon-Sup Han. Section F. 2. Carnegie Mellon. Today: Stacks. News. Stack discipline review. Quick review of registers and assembly. Midterm Review. 15-213: Introduction to Computer Systems . October 15, 2012. Instructor. :. Agenda. Midterm tomorrow!. Cheat sheet: One 8.5 x 11, front and back. Review. Everything up to caching. Questions. If you’re trying to sell your house fast, you may have come across the term cash home buyer. The term refers to real estate investors who buy homes for cash. We are 412 Houses, leading cash home buyers in Pittsburgh, and we help people sell their distressed properties. https://www.412houses.com/