Departme partment nt of Computer puter Science nce Undergr dergrad aduat uate e Events ts for Sept 10-14 14 Mor ore e details ls @ https ps://ww ://www.cs.ub w.cs.ubc. c.ca ca/s /stud tudent ents/ s/und under ergra grad/l d/life ife/up /upco comin ming-ev event ents TELUS US Info Sessi sion on Tri-Me Ment ntori oring ng Student ent Orientatio ntation Date: e: Mon. Sept. 10 Date: e: Tues. s. Sept. 11 Time: e: 5:30 – 7:30 pm Time: e: 5 5:15 – 6:30 pm Locati cation on: Wesb sbroo ook 100 100 Locati cation on: DMP 110 Resu sume e Writing ing Work rkshop shop (for r non- Deloitt oitte e Info Se Sess ssion on coop ops) s) Date: e: Tues. s. Sept. 11 Date: e: Thur urs. s. Sept. 13 Time: e: 6:00 – 8:00 pm Time: e: 12:30 0 – 1:45 pm Locati cation on: Henr nry y Angus us Room m 098 Locati cation on: DMP 101 101 Capgem pgemini ini Info Sessio sion Wome men n in Games es Panel Date: Fri. Sept. 14 Date: e: Wed. . Se Sept 12 Time: e: 2:00 – 5:00 pm Time: e: 5:30 – 9:00 pm Loca cation on: Downtown ntown Va Vancou couver ver Locati cation on: EA Bu Burna naby by Studios os (RSVP SVP req’d by Sept. 12)
AI I App pplica catio tions ns Computer ter Sc Science ce cpsc3 c322 22, , Lectur ture e 3 Sept, t, 10, 2012 CPSC 322, Lecture 3 Slide 2
Modules dules we'l 'll l cover er in th this course: se: R&Rsys sys En Enviro ronm nmen ent Stochastic Deterministic Pr Problem em Arc Consistency Constraint Vars + Satisfaction Search Constraints Stati atic Belief Nets Logics Query Var. Elimination Search Decision Nets Sequenti ntial al STRIPS Var. Elimination Planning Markov Processes Search Representation Value Iteration Reasoning CPSC 322, Lecture 2 Slide 3 Technique
Modules dules we'l 'll l cover er in th this course: se: R&Rsys sys En Enviro ronm nmen ent Stochastic Deterministic Pr Problem em Arc Consistency Constraint Vars + Satisfaction Search Constraints Stati atic Belief Nets Logics Query Var. Elimination Search Decision Nets Sequenti ntial al STRIPS Var. Elimination Planning Markov Processes Search Value Iteration CPSC 322, Lecture 2 Slide 4
(Ad Adve versarial) rsarial) Se Search ch: : Checkers ckers Gam ame e pl play ayin ing was one of the first tasks undertaken in AI Arthur Ar ur Sa Samuel at IBM wrote programs to play checkers (1950s) • initially, they played at a strong amateur level • however, they used some (simple) machine learning techniques, and soon outperformed Samuel Source: IBM Research Chinook’s program was declared the Man- Machine World Champion in checkers in 1994! …and complete etely ly solve ved by a program in 2007! CPSC 322, Lecture 3 Slide 5
(Ad Adve versarial) rsarial) Se Search ch: : Chess ss In 1996 and 1997, Gary Kasparov, the world chess grandmaster played two tournaments against Deep Blue, a program written by researchers at IBM Source: IBM Research CPSC 322, Lecture 3 Slide 6
(Ad Adve versarial) rsarial) Se Search ch: : Chess ss Deep Blue’s Results in the first tournament: • won 1 game, lost 3 and tied 1 first time a reigning world champion lost to a computer CPSC 322, Lecture 3 Source: CNN Slide 7
(Ad Adve versarial) rsarial) Se Search ch: : Chess ss Deep Blue’s Results in the second tournament: • second tournament: won 3 games, lost 2, tied 1 • 30 CPUs + 480 chess processors • Searched 126.000.000 nodes per sec • Generated 30 billion positions per move reaching depth 14 routinely CPSC 322, Lecture 3 Slide 8
Sample mple A* applications lications • An An Ef Efficie cient nt A* A* Se Search Al Algorith ithm Fo For St Statistical stical Machine Translation. 2001 • Th The General aliz ized ed A* A* Ar Architec tectur ture. Journal of Artificial Intelligence Research (2007) • Machine Vision … Here we consider a new compositional model for finding salient curves. • Fa Factor tored d A* A*searc rch h for models s over sequences nces and trees International Conference on AI. 2003…. It starts saying… The primary challenge when using A* search is to find heuristic functions that simultaneously are admissible, close to actual completion costs, and efficient to calculate… applied to NLP and BioInformatics CPSC 322, Lecture 9 Slide 9
Modules dules we'l 'll l cover er in th this course: se: R&Rsys sys En Enviro ronm nmen ent Stochastic Deterministic Pr Problem em Arc Consistency Constraint Vars + Satisfaction Search Constraints Stati atic Belief Nets Logics Query Var. Elimination Search Decision Nets Sequenti ntial al STRIPS Var. Elimination Planning Markov Processes Search Value Iteration CPSC 322, Lecture 2 Slide 10
CSP SPs: s: Crossword ssword Pu Puzzl zzles es Source: Michael Littman CPSC 322, Lecture 3 Slide 11
CSP SPs: s: Radio io link k fr frequency uency ass ssignment ignment Assigning frequencies to a set of radio links defined between pairs of sites in order to avoid d interfe rfere renc nces es. Constraints on frequency depend on positio tion of the links ks and on physi sica cal l enviro ronme ment nt . Source: INRIA Sample Constraint network CPSC 322, Lecture 3 Slide 12
Ex Example le: : SL SLS f S for RNA A secon onda dary ry structu cture re design RNA strand made up of four bases: cytosine (C), guanine (G), adenine (A), and uracil (U) 2D/3D structure RNA strand folds into RNA strand is important for its function GUCCCAUAGGAUGUCCCAUAGGA Predicting structure for a strand is “easy”: O(n 3 ) Easy Hard But what if we want a strand that folds Secondary structure into a certain structure? On of the Best algorithm to date: Local search algorithm RNA-SSD developed at UBC [Andronescu, Fejes, Hutter, Condon, and Hoos, Journal of Molecular Biology, 2004] CPSC 322, Lecture 1 13
Constraint nstraint optimi timizatio zation n problems blems Optimization under side constraints (similar to CSP) E.g. mixed integer programming (software: IBM CPLEX) • Linear program: max c T x such that Ax ≤ b • Mixed integer program: additional constraints, x i Z (integers) • NP-hard, widely used in operations research and in industry Transportation/Logistics: Supply chain Production planning SNCF, United Airlines management and optimization: UPS, United States software: Airbus, Dell, Porsche, Postal Service, … Oracle, Thyssen Krupp, SAP,… Toyota, Nissan, ... CPSC 322, Lecture 1 14
Modules dules we'l 'll l cover er in th this course: se: R&Rsys sys En Enviro ronm nmen ent Stochastic Deterministic Pr Problem em Arc Consistency Constraint Vars + Satisfaction Search Constraints Stati atic Belief Nets Logics Query Var. Elimination Search Decision Nets Sequenti ntial al STRIPS Var. Elimination Planning Markov Processes Search Value Iteration CPSC 322, Lecture 2 Slide 15
CSP SP/lo /logic: gic: fo formal al ve verification ification Hardware verification Software verification (e.g., IBM) (small to medium programs) Most progress in the last 10 years based on: Encodings into propositional satisfiability (SAT) CPSC 322, Lecture 1 16
Logic: gic: Cyc ycSecure Secure “ scans s a computer ter netwo work rk to build a f formal mal represe sent ntati ation on of the network, based on Cyc’s pre -existing ontology of networking, security, and computing concepts: Excerpted from: Shepard et al., 2005 This formal representation also allows users to interact directly with the model of the network, allowing testing of proposed changes.” • Kn Knowl wledge dge Repres esen entat tatio ion • Se Semantic tic Web ! CPSC 322, Lecture 3 Slide 17
Modules dules we'l 'll l cover er in th this course: se: R&Rsys sys En Enviro ronm nmen ent Stochastic Deterministic Pr Problem em Arc Consistency Constraint Vars + Satisfaction Search Constraints Stati atic Belief Nets Logics Query Var. Elimination Search Decision Nets Sequenti ntial al STRIPS Var. Elimination Planning Markov Processes Search Value Iteration CPSC 322, Lecture 2 Slide 18
Pl Planning anning & Sc & Scheduling: eduling: Logistics istics Dynamic Analysis and Replanning Tool (Cross & Walker) • logistics planning and scheduling for military transport • used in the 1991 Gulf War by the US • problems had 50,000 entities (e.g., vehicles); different starting points and destinations Same techniques can be used for non-military applications: e.g., Emergency Evacuation CPSC 322, Lecture 3 Slide 19 Source: DARPA
Pl Planning: anning: Sp Spacecraft cecraft Control trol NASA: Deep Space One spacecraft operated autonomously for two days in May, 1999: • determined its precise position using stars and asteriods despite a malfunctioning ultraviolet detector • planned the necessary course adjustment • fired the ion propulsion system to make this adjustment For another space application see the Spike system for the Hubble telescope Source: NASA CPSC 322, Lecture 3 Slide 20
Source: CPSC 322, Lecture 1 Slide 21 cs221 stanford
Recommend
More recommend
