Sum-Product: Message Passing Belief Propagation
|
|
- Anissa Richardson
- 5 years ago
- Views:
Transcription
1 Sum-Product: Message Passing Belief Propagation Probabilistic Graphical Models Sharif University of Technology Spring 2017 Soleymani
2 All single-node marginals If we need the full set of marginals, repeating elimination algorithm for each individual variable is wasteful It does not share intermediate terms Message-passing algorithms on graphs (messages are the shared intermediate terms). sum-product and junction tree upon convergence of the algorithms, we obtain marginal probabilities for all cliques of the original graph. 2
3 Tree Sum-product work only in trees (and we will see it also work on tree-like graphs) Undirected tree A unique path between any pair of nodes Directed tree All nodes have one parent expect to the root 3
4 Parameterization Consider a tree T(V, E) Potential functions: φ x i, φ(x i, x j ) P x = 1 Z i V φ x i i,j E φ x i, x j In directed graphs: P x = P(x r ) P x j x i i,j E φ x r = P(x r ), i r, φ x i = 1 φ x i, x j = P(x j x i ) (x i is the parent of x j ) Z = 1 When we have evidence on variable x i as x i = x i in all factors in which it appears by x i x i we replace 4
5 Sum-product: elimination view Query node r Elimination order: inverse of the topological order Starts from leaves and generates elimination cliques of size at most two Elimination of each node can be considered as messagepassing (or Belief Propagation): Elimination on trees is equivalent to message passing along tree branches Instead of the node elimination, we preserve the node and compute a message from it to its parent This message is equivalent to the factor resulted from the elimination of that node and all of the nodes in its subtree 5
6 Messages root Message that j sends to i 6
7 Messages on a tree Messages can be reused to find probabilities on different query variables. Messages on the tree provide a data structure for caching computations. X 2 We need m 32 (x 2 ) to find both P(X 1 ) and P(X 2 ) X 1 X 3 X 4 X 5 7
8 Messages and marginal distribution Message that X j sends to X i m ji x i = x j φ x j φ x i, x j k N(j)\i m kj (x j ) a function of only x i p x r φ x r k N(r) m kr (x r ) 8
9 Messages and marginal: Example m 12 x 2 = x 1 φ x 1 φ x 1, x 2 p x 2 φ x 2 m 12 (x 2 )m 32 (x 2 )m 42 (x 2 ) 9
10 Computing all node marginals We can compute over all possible elimination order (generating only elimination cliques of size 2) by only computing all possible messages (2 E ) To allow all nodes can be the root, we just need to compute 2 E messages Messages can be reused Instead of running the elimination algorithm N times Dynamic programming approach 2-Pass algorithm that saves and uses messages A pair of messages (one for each direction) have been computed for each edge 10
11 Messages required to compute all node marginals 11
12 A two-pass message-passing schedule Arbitrarily pick a node as the root First pass: starting at the leaves and proceeds inward each node passes a message to its parent. continues until the root has obtained messages from all of its adjoining nodes. Second pass: starting at the root and passing the messages back out messages are passed in the reverse direction. continues until all leaves have received their messages. 12
13 Asynchronous two-pass message-passing 13 First pass: upward Second pass: downward
14 Sum-product algorithm: example m 21 (x 1 ) m 21 (x 1 ) 14
15 Sum-product algorithm: example m 21 (x 1 ) 15
16 Parallel message-passing Message-passing protocol: a node can send a message to a neighboring node when and only when it has received messages from all of its other neighbors Correctness of parallel message-passing on trees The synchronous implementation is non-blocking Theorem: The message-passing guarantees obtaining all marginals in the tree 16
17 Parallel message passing: Example 17
18 Tree-like graphs Sum-product message passing idea can also be extended to work in tree-like graphs (e.g., polytrees) too. Although the undirected marginalized graphs resulted from polytrees are not tree, the corresponding factor graph is a tree 18 Polytree Nodes can have multiple parents Moralized graph Factor graph
19 Recall: Factor graph φ x 1, x 2, x 3 = f a (x 1, x 2 )f b (x 1, x 3 )f c (x 2, x 3 ) φ x 1, x 2, x 3 = f x 1, x 2, x 3 19
20 Sum-product on factor trees Factor tree: a factor graph with no loop Two types of messages: Message that flows from variable node i to factor node s: v is x i = t N i {s} μ ti (x i ) Message that flows from factor node s to variable node i: μ si x i = x N s {i} f s x N(s) j N s {i} v js (x j ) 20
21 Sum-product on factor trees The introduced message-passing schedule for trees can also be used on factor trees When the messages from all the neighbors of a node is received, the marginal probability will be: P x i s N i μ si x i 21 P x i v is (x i )μ si (x i ) s N i s is a factor node that is neighbor of X i
22 The relation between sum-product on factor trees and sum-product on undirected trees Relation of m messages of sum-product algorithm for undirected trees and μ messages of sum-product algorithm for factor trees μ si x i = x N s {i} f s x N(s) j N s {i} v js (x j ) = x j φ(x i, x j )v js (x j ) = φ(x i, x j ) μ tj (x j ) x j t N j {s} = φ(x i )φ(x i, x j ) μ tj (x j ) x j t N j {s} 22 N j = N j {factor corresponding to φ(x j )}
23 Example 23
24 References D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009, Chapter 10. M.I. Jordan, An Introduction to Probabilistic Graphical Models, Chapter 4. 24
Sum-Product: Message Passing Belief Propagation
Sum-Product: Message Passing Belief Propagation 40-956 Advanced Topics in AI: Probabilistic Graphical Models Sharif University of Technology Soleymani Spring 2015 All single-node marginals If we need the
More informationFactor Graphs. Seungjin Choi
Factor Graphs Seungjin Choi Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea seungjin@postech.ac.kr 1 / 17 Tanner Graphs
More informationInference in Bayesian Networks
Andrea Passerini passerini@disi.unitn.it Machine Learning Inference in graphical models Description Assume we have evidence e on the state of a subset of variables E in the model (i.e. Bayesian Network)
More informationExact Inference. Factor Graphs through Max-Sum Algorithm Figures from Bishop PRML Sec. 8.3/8.4. x 3. f s. x 2. x 1
Exact Inference x 1 x 3 x 2 f s Geoffrey Roeder roeder@cs.toronto.edu 8 February 2018 Factor Graphs through Max-Sum Algorithm Figures from Bishop PRML Sec. 8.3/8.4 Building Blocks UGMs, Cliques, Factor
More informationCMPSCI 311: Introduction to Algorithms Second Midterm Practice Exam SOLUTIONS
CMPSCI 311: Introduction to Algorithms Second Midterm Practice Exam SOLUTIONS November 17, 2016. Name: ID: Instructions: Answer the questions directly on the exam pages. Show all your work for each question.
More informationGibbs Fields: Inference and Relation to Bayes Networks
Statistical Techniques in Robotics (16-831, F10) Lecture#08 (Thursday September 16) Gibbs Fields: Inference and Relation to ayes Networks Lecturer: rew agnell Scribe:ebadeepta ey 1 1 Inference on Gibbs
More informationCOMP90051 Statistical Machine Learning
COMP90051 Statistical Machine Learning Semester 2, 2017 Lecturer: Trevor Cohn 22. PGM Probabilistic Inference Probabilistic inference on PGMs Computing marginal and conditional distributions from the joint
More informationMachine Learning. Graphical Models. Marc Toussaint University of Stuttgart Summer 2015
Machine Learning Graphical Models Marc Toussaint University of Stuttgart Summer 2015 Outline A. Introduction Motivation and definition of Bayes Nets Conditional independence in Bayes Nets Examples B. Inference
More informationUGM Crash Course: Conditional Inference and Cutset Conditioning
UGM Crash Course: Conditional Inference and Cutset Conditioning Julie Nutini August 19 th, 2015 1 / 25 Conditional UGM 2 / 25 We know the value of one or more random variables i.e., we have observations,
More informationExact Inference (9/30/13) 2 A brief review of Forward-Backward and EM for HMMs
STA561: Probabilistic machine learning Exact Inference (9/30/13) Lecturer: Barbara Engelhardt Scribes: Jiawei Liang, He Jiang, Brittany Cohen 1 Validation for Clustering If we have two centroids, η 1 and
More informationOutline. Objective. Previous Results Our Results Discussion Current Research. 1 Motivation. 2 Model. 3 Results
On Threshold Esteban 1 Adam 2 Ravi 3 David 4 Sergei 1 1 Stanford University 2 Harvard University 3 Yahoo! Research 4 Carleton College The 8th ACM Conference on Electronic Commerce EC 07 Outline 1 2 3 Some
More informationCSE 100: TREAPS AND RANDOMIZED SEARCH TREES
CSE 100: TREAPS AND RANDOMIZED SEARCH TREES Midterm Review Practice Midterm covered during Sunday discussion Today Run time analysis of building the Huffman tree AVL rotations and treaps Huffman s algorithm
More informationProject Management Techniques (PMT)
Project Management Techniques (PMT) Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT) are 2 main basic techniques used in project management. Example: Construction of a house.
More informationPrinciples of Program Analysis: Algorithms
Principles of Program Analysis: Algorithms Transparencies based on Chapter 6 of the book: Flemming Nielson, Hanne Riis Nielson and Chris Hankin: Principles of Program Analysis. Springer Verlag 2005. c
More informationAnother Variant of 3sat. 3sat. 3sat Is NP-Complete. The Proof (concluded)
3sat k-sat, where k Z +, is the special case of sat. The formula is in CNF and all clauses have exactly k literals (repetition of literals is allowed). For example, (x 1 x 2 x 3 ) (x 1 x 1 x 2 ) (x 1 x
More informationLecture 17: More on Markov Decision Processes. Reinforcement learning
Lecture 17: More on Markov Decision Processes. Reinforcement learning Learning a model: maximum likelihood Learning a value function directly Monte Carlo Temporal-difference (TD) learning COMP-424, Lecture
More informationWhat is Greedy Approach? Control abstraction for Greedy Method. Three important activities
0-0-07 What is Greedy Approach? Suppose that a problem can be solved by a sequence of decisions. The greedy method has that each decision is locally optimal. These locally optimal solutions will finally
More informationBioinformatics - Lecture 7
Bioinformatics - Lecture 7 Louis Wehenkel Department of Electrical Engineering and Computer Science University of Liège Montefiore - Liège - November 20, 2007 Find slides: http://montefiore.ulg.ac.be/
More informationInformation Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay
Information Theory and Coding Prof. S. N. Merchant Department of Electrical Engineering Indian Institute of Technology, Bombay Lecture - 15 Adaptive Huffman Coding Part I Huffman code are optimal for a
More informationThe von Mises Graphical Model: Expectation Propagation for Inference
The von Mises Graphical Model: Expectation Propagation for Inference Narges Razavian, Hetunandan Kamisetty, Christopher James Langmead September 2011 CMU-CS-11-130 CMU-CB-11-102 School of Computer Science
More informationSequential Decision Making
Sequential Decision Making Dynamic programming Christos Dimitrakakis Intelligent Autonomous Systems, IvI, University of Amsterdam, The Netherlands March 18, 2008 Introduction Some examples Dynamic programming
More informationCrash-tolerant Consensus in Directed Graph Revisited
Crash-tolerant Consensus in Directed Graph Revisited Ashish Choudhury Gayathri Garimella Arpita Patra Divya Ravi Pratik Sarkar Abstract Fault-tolerant distributed consensus is a fundamental problem in
More information1) S = {s}; 2) for each u V {s} do 3) dist[u] = cost(s, u); 4) Insert u into a 2-3 tree Q with dist[u] as the key; 5) for i = 1 to n 1 do 6) Identify
CSE 3500 Algorithms and Complexity Fall 2016 Lecture 17: October 25, 2016 Dijkstra s Algorithm Dijkstra s algorithm for the SSSP problem generates the shortest paths in nondecreasing order of the shortest
More informationMachine Learning
Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University October 13, 2011 Today: Graphical models Bayes Nets: Conditional independencies Inference Learning Readings:
More informationCSE 21 Winter 2016 Homework 6 Due: Wednesday, May 11, 2016 at 11:59pm. Instructions
CSE 1 Winter 016 Homework 6 Due: Wednesday, May 11, 016 at 11:59pm Instructions Homework should be done in groups of one to three people. You are free to change group members at any time throughout the
More informationMachine Learning in Computer Vision Markov Random Fields Part II
Machine Learning in Computer Vision Markov Random Fields Part II Oren Freifeld Computer Science, Ben-Gurion University March 22, 2018 Mar 22, 2018 1 / 40 1 Some MRF Computations 2 Mar 22, 2018 2 / 40 Few
More informationProbabilistic Graphical Models
CS420, Machine Learning, Lecture 8 Probabilistic Graphical Models Weinan Zhang Shanghai Jiao Tong University http://wnzhang.net http://wnzhang.net/teaching/cs420/index.html Content of This Lecture Introduction
More informationIEOR E4004: Introduction to OR: Deterministic Models
IEOR E4004: Introduction to OR: Deterministic Models 1 Dynamic Programming Following is a summary of the problems we discussed in class. (We do not include the discussion on the container problem or the
More informationUniversity of British Columbia. Abstract. problems represented as inuence diagrams. An algorithm is given
Flexible Policy Construction by Information Renement Michael C. Horsch horsch@cs.ubc.ca David Poole poole@cs.ubc.ca Department of Computer Science University of British Columbia 2366 Main Mall, Vancouver,
More informationSublinear Time Algorithms Oct 19, Lecture 1
0368.416701 Sublinear Time Algorithms Oct 19, 2009 Lecturer: Ronitt Rubinfeld Lecture 1 Scribe: Daniel Shahaf 1 Sublinear-time algorithms: motivation Twenty years ago, there was practically no investigation
More informationFactor Graphs, the Sum-Product Algorithm and TrueSkill TM
Factor Graphs, the Sum-Product Algorithm and TrueSkill TM Kuhwan Jeong 1 1 Department of Statistics, Seoul National University, South Korea July, 2018 1 / 17 Introduction x i : a variable taking on values
More informationGame Theory: Normal Form Games
Game Theory: Normal Form Games Michael Levet June 23, 2016 1 Introduction Game Theory is a mathematical field that studies how rational agents make decisions in both competitive and cooperative situations.
More informationPath-properties of the tree-valued Fleming-Viot process
Path-properties of the tree-valued Fleming-Viot process Peter Pfaffelhuber Joint work with Andrej Depperschmidt and Andreas Greven Luminy, 492012 The Moran model time t As every population model, the Moran
More informationMultirate Multicast Service Provisioning I: An Algorithm for Optimal Price Splitting Along Multicast Trees
Mathematical Methods of Operations Research manuscript No. (will be inserted by the editor) Multirate Multicast Service Provisioning I: An Algorithm for Optimal Price Splitting Along Multicast Trees Tudor
More informationStochastic Dual Dynamic Programming
1 / 43 Stochastic Dual Dynamic Programming Operations Research Anthony Papavasiliou 2 / 43 Contents [ 10.4 of BL], [Pereira, 1991] 1 Recalling the Nested L-Shaped Decomposition 2 Drawbacks of Nested Decomposition
More informationAN ALGORITHM FOR FINDING SHORTEST ROUTES FROM ALL SOURCE NODES TO A GIVEN DESTINATION IN GENERAL NETWORKS*
526 AN ALGORITHM FOR FINDING SHORTEST ROUTES FROM ALL SOURCE NODES TO A GIVEN DESTINATION IN GENERAL NETWORKS* By JIN Y. YEN (University of California, Berkeley) Summary. This paper presents an algorithm
More informationEssays on Some Combinatorial Optimization Problems with Interval Data
Essays on Some Combinatorial Optimization Problems with Interval Data a thesis submitted to the department of industrial engineering and the institute of engineering and sciences of bilkent university
More informationDifferent Monotonicity Definitions in stochastic modelling
Different Monotonicity Definitions in stochastic modelling Imène KADI Nihal PEKERGIN Jean-Marc VINCENT ASMTA 2009 Plan 1 Introduction 2 Models?? 3 Stochastic monotonicity 4 Realizable monotonicity 5 Relations
More informationV. Lesser CS683 F2004
The value of information Lecture 15: Uncertainty - 6 Example 1: You consider buying a program to manage your finances that costs $100. There is a prior probability of 0.7 that the program is suitable in
More informationThe exam is closed book, closed calculator, and closed notes except your three crib sheets.
CS 188 Spring 2016 Introduction to Artificial Intelligence Final V2 You have approximately 2 hours and 50 minutes. The exam is closed book, closed calculator, and closed notes except your three crib sheets.
More information91.420/543: Artificial Intelligence UMass Lowell CS Fall 2010
91.420/543: Artificial Intelligence UMass Lowell CS Fall 2010 Lecture 17 & 18: Markov Decision Processes Oct 12 13, 2010 A subset of Lecture 9 slides from Dan Klein UC Berkeley Many slides over the course
More informationGlobal Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs
Teaching Note October 26, 2007 Global Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs Xinhua Zhang Xinhua.Zhang@anu.edu.au Research School of Information Sciences
More informationThe EM algorithm for HMMs
The EM algorithm for HMMs Michael Collins February 22, 2012 Maximum-Likelihood Estimation for Fully Observed Data (Recap from earlier) We have fully observed data, x i,1... x i,m, s i,1... s i,m for i
More informationSMT and POR beat Counter Abstraction
SMT and POR beat Counter Abstraction Parameterized Model Checking of Threshold-Based Distributed Algorithms Igor Konnov Helmut Veith Josef Widder Alpine Verification Meeting May 4-6, 2015 Igor Konnov 2/64
More informationAnother Variant of 3sat
Another Variant of 3sat Proposition 32 3sat is NP-complete for expressions in which each variable is restricted to appear at most three times, and each literal at most twice. (3sat here requires only that
More informationPARELLIZATION OF DIJKSTRA S ALGORITHM: COMPARISON OF VARIOUS PRIORITY QUEUES
PARELLIZATION OF DIJKSTRA S ALGORITHM: COMPARISON OF VARIOUS PRIORITY QUEUES WIKTOR JAKUBIUK, KESHAV PURANMALKA 1. Introduction Dijkstra s algorithm solves the single-sourced shorest path problem on a
More informationYou Have an NP-Complete Problem (for Your Thesis)
You Have an NP-Complete Problem (for Your Thesis) From Propositions 27 (p. 242) and Proposition 30 (p. 245), it is the least likely to be in P. Your options are: Approximations. Special cases. Average
More informationIt is used when neither the TX nor RX knows anything about the statistics of the source sequence at the start of the transmission
It is used when neither the TX nor RX knows anything about the statistics of the source sequence at the start of the transmission -The code can be described in terms of a binary tree -0 corresponds to
More informationOptimal Security Liquidation Algorithms
Optimal Security Liquidation Algorithms Sergiy Butenko Department of Industrial Engineering, Texas A&M University, College Station, TX 77843-3131, USA Alexander Golodnikov Glushkov Institute of Cybernetics,
More informationMarkov Decision Processes
Markov Decision Processes Ryan P. Adams COS 324 Elements of Machine Learning Princeton University We now turn to a new aspect of machine learning, in which agents take actions and become active in their
More informationRecall: Data Flow Analysis. Data Flow Analysis Recall: Data Flow Equations. Forward Data Flow, Again
Data Flow Analysis 15-745 3/24/09 Recall: Data Flow Analysis A framework for proving facts about program Reasons about lots of little facts Little or no interaction between facts Works best on properties
More informationThe exam is closed book, closed calculator, and closed notes except your one-page crib sheet.
CS 188 Spring 2015 Introduction to Artificial Intelligence Midterm 1 You have approximately 2 hours and 50 minutes. The exam is closed book, closed calculator, and closed notes except your one-page crib
More informationThe Values of Information and Solution in Stochastic Programming
The Values of Information and Solution in Stochastic Programming John R. Birge The University of Chicago Booth School of Business JRBirge ICSP, Bergamo, July 2013 1 Themes The values of information and
More informationReinforcement Learning
Reinforcement Learning Basic idea: Receive feedback in the form of rewards Agent s utility is defined by the reward function Must (learn to) act so as to maximize expected rewards Grid World The agent
More informationMaximizing the Spread of Influence through a Social Network Problem/Motivation: Suppose we want to market a product or promote an idea or behavior in
Maximizing the Spread of Influence through a Social Network Problem/Motivation: Suppose we want to market a product or promote an idea or behavior in a society. In order to do so, we can target individuals,
More informationNon-Deterministic Search
Non-Deterministic Search MDP s 1 Non-Deterministic Search How do you plan (search) when your actions might fail? In general case, how do you plan, when the actions have multiple possible outcomes? 2 Example:
More informationCSE 473: Artificial Intelligence
CSE 473: Artificial Intelligence Markov Decision Processes (MDPs) Luke Zettlemoyer Many slides over the course adapted from Dan Klein, Stuart Russell or Andrew Moore 1 Announcements PS2 online now Due
More informationDiusion in the French Input-output Network
Diusion in the Input-output Contreras martha.alatriste@ehess.fr Aix-Marseille School of Economics, CNRS, EHESS Getting Inside the Black Box: Technological Evolution Economic Growth Santa Fe Institute Aug.
More informationMulti-agent influence diagrams for representing and solving games
Games and Economic Behavior 45 (2003) 181 221 www.elsevier.com/locate/geb Multi-agent influence diagrams for representing and solving games Daphne Koller a, and Brian Milch b a Stanford University, Computer
More informationLecture 6. 1 Polynomial-time algorithms for the global min-cut problem
ORIE 633 Network Flows September 20, 2007 Lecturer: David P. Williamson Lecture 6 Scribe: Animashree Anandkumar 1 Polynomial-time algorithms for the global min-cut problem 1.1 The global min-cut problem
More informationZero-Knowledge Arguments for Lattice-Based Accumulators: Logarithmic-Size Ring Signatures and Group Signatures without Trapdoors
Zero-Knowledge Arguments for Lattice-Based Accumulators: Logarithmic-Size Ring Signatures and Group Signatures without Trapdoors Benoît Libert 1 San Ling 2 Khoa Nguyen 2 Huaxiong Wang 2 1 Ecole Normale
More informationA relation on 132-avoiding permutation patterns
Discrete Mathematics and Theoretical Computer Science DMTCS vol. VOL, 205, 285 302 A relation on 32-avoiding permutation patterns Natalie Aisbett School of Mathematics and Statistics, University of Sydney,
More informationHeaps. Heap/Priority queue. Binomial heaps: Advanced Algorithmics (4AP) Heaps Binary heap. Binomial heap. Jaak Vilo 2009 Spring
.0.00 Heaps http://en.wikipedia.org/wiki/category:heaps_(structure) Advanced Algorithmics (4AP) Heaps Jaak Vilo 00 Spring Binary heap http://en.wikipedia.org/wiki/binary_heap Binomial heap http://en.wikipedia.org/wiki/binomial_heap
More informationCS Homework 4: Expectations & Empirical Distributions Due Date: October 9, 2018
CS1450 - Homework 4: Expectations & Empirical Distributions Due Date: October 9, 2018 Question 1 Consider a set of n people who are members of an online social network. Suppose that each pair of people
More informationHandout 4: Deterministic Systems and the Shortest Path Problem
SEEM 3470: Dynamic Optimization and Applications 2013 14 Second Term Handout 4: Deterministic Systems and the Shortest Path Problem Instructor: Shiqian Ma January 27, 2014 Suggested Reading: Bertsekas
More informationTug of War Game. William Gasarch and Nick Sovich and Paul Zimand. October 6, Abstract
Tug of War Game William Gasarch and ick Sovich and Paul Zimand October 6, 2009 To be written later Abstract Introduction Combinatorial games under auction play, introduced by Lazarus, Loeb, Propp, Stromquist,
More informationQ1. [?? pts] Search Traces
CS 188 Spring 2010 Introduction to Artificial Intelligence Midterm Exam Solutions Q1. [?? pts] Search Traces Each of the trees (G1 through G5) was generated by searching the graph (below, left) with a
More informationHeaps
AdvancedAlgorithmics (4AP) Heaps Jaak Vilo 2009 Spring Jaak Vilo MTAT.03.190 Text Algorithms 1 Heaps http://en.wikipedia.org/wiki/category:heaps_(structure) Binary heap http://en.wikipedia.org/wiki/binary_heap
More informationLevin Reduction and Parsimonious Reductions
Levin Reduction and Parsimonious Reductions The reduction R in Cook s theorem (p. 266) is such that Each satisfying truth assignment for circuit R(x) corresponds to an accepting computation path for M(x).
More informationMengdi Wang. July 3rd, Laboratory for Information and Decision Systems, M.I.T.
Practice July 3rd, 2012 Laboratory for Information and Decision Systems, M.I.T. 1 2 Infinite-Horizon DP Minimize over policies the objective cost function J π (x 0 ) = lim N E w k,k=0,1,... DP π = {µ 0,µ
More informationOptimization Methods in Management Science
Problem Set Rules: Optimization Methods in Management Science MIT 15.053, Spring 2013 Problem Set 6, Due: Thursday April 11th, 2013 1. Each student should hand in an individual problem set. 2. Discussing
More informationCS188 Spring 2012 Section 4: Games
CS188 Spring 2012 Section 4: Games 1 Minimax Search In this problem, we will explore adversarial search. Consider the zero-sum game tree shown below. Trapezoids that point up, such as at the root, represent
More informationOptimal Satisficing Tree Searches
Optimal Satisficing Tree Searches Dan Geiger and Jeffrey A. Barnett Northrop Research and Technology Center One Research Park Palos Verdes, CA 90274 Abstract We provide an algorithm that finds optimal
More informationFibonacci Heaps Y Y o o u u c c an an s s u u b b m miitt P P ro ro b blle e m m S S et et 3 3 iin n t t h h e e b b o o x x u u p p fro fro n n tt..
Fibonacci Heaps You You can can submit submit Problem Problem Set Set 3 in in the the box box up up front. front. Outline for Today Review from Last Time Quick refresher on binomial heaps and lazy binomial
More informationAn Experimental Study of the Behaviour of the Proxel-Based Simulation Algorithm
An Experimental Study of the Behaviour of the Proxel-Based Simulation Algorithm Sanja Lazarova-Molnar, Graham Horton Otto-von-Guericke-Universität Magdeburg Abstract The paradigm of the proxel ("probability
More informationBinary Decision Diagrams
Binary Decision Diagrams Hao Zheng Department of Computer Science and Engineering University of South Florida Tampa, FL 33620 Email: zheng@cse.usf.edu Phone: (813)974-4757 Fax: (813)974-5456 Hao Zheng
More informationLecture outline W.B.Powell 1
Lecture outline What is a policy? Policy function approximations (PFAs) Cost function approximations (CFAs) alue function approximations (FAs) Lookahead policies Finding good policies Optimizing continuous
More informationLecture 23: April 10
CS271 Randomness & Computation Spring 2018 Instructor: Alistair Sinclair Lecture 23: April 10 Disclaimer: These notes have not been subjected to the usual scrutiny accorded to formal publications. They
More informationUNIT 2. Greedy Method GENERAL METHOD
UNIT 2 GENERAL METHOD Greedy Method Greedy is the most straight forward design technique. Most of the problems have n inputs and require us to obtain a subset that satisfies some constraints. Any subset
More informationProbabilistic Robotics: Probabilistic Planning and MDPs
Probabilistic Robotics: Probabilistic Planning and MDPs Slide credits: Wolfram Burgard, Dieter Fox, Cyrill Stachniss, Giorgio Grisetti, Maren Bennewitz, Christian Plagemann, Dirk Haehnel, Mike Montemerlo,
More informationA combinatorial prediction market for the U.S. Elections
A combinatorial prediction market for the U.S. Elections Miroslav Dudík Thanks: S Lahaie, D Pennock, D Rothschild, D Osherson, A Wang, C Herget Polling accurate, but costly limited range of questions
More informationOutline for Today. Quick refresher on binomial heaps and lazy binomial heaps. An important operation in many graph algorithms.
Fibonacci Heaps Outline for Today Review from Last Time Quick refresher on binomial heaps and lazy binomial heaps. The Need for decrease-key An important operation in many graph algorithms. Fibonacci Heaps
More informationCS221 / Spring 2018 / Sadigh. Lecture 9: Games I
CS221 / Spring 2018 / Sadigh Lecture 9: Games I Course plan Search problems Markov decision processes Adversarial games Constraint satisfaction problems Bayesian networks Reflex States Variables Logic
More informationBinary Decision Diagrams
Binary Decision Diagrams Hao Zheng Department of Computer Science and Engineering University of South Florida Tampa, FL 33620 Email: zheng@cse.usf.edu Phone: (813)974-4757 Fax: (813)974-5456 Hao Zheng
More informationAdvanced Algorithmics (4AP) Heaps
Advanced Algorithmics (4AP) Heaps Jaak Vilo 2009 Spring Jaak Vilo MTAT.03.190 Text Algorithms 1 Heaps http://en.wikipedia.org/wiki/category:heaps_(structure) Binary heap http://en.wikipedia.org/wiki/binary
More informationDesigning efficient market pricing mechanisms
Designing efficient market pricing mechanisms Volodymyr Kuleshov Gordon Wilfong Department of Mathematics and School of Computer Science, McGill Universty Algorithms Research, Bell Laboratories August
More informationAbout the Risk Quantification of Technical Systems
About the Risk Quantification of Technical Systems Magda Schiegl ASTIN Colloquium 2013, The Hague Outline Introduction / Overview Fault Tree Analysis (FTA) Method of quantitative risk analysis Results
More informationCS 188: Artificial Intelligence Spring Announcements
CS 188: Artificial Intelligence Spring 2011 Lecture 9: MDPs 2/16/2011 Pieter Abbeel UC Berkeley Many slides over the course adapted from either Dan Klein, Stuart Russell or Andrew Moore 1 Announcements
More informationSquare Grid Benchmarks for Source-Terminal Network Reliability Estimation
Square Grid Benchmarks for Source-Terminal Network Reliability Estimation Roger Paredes Leonardo Duenas-Osorio Rice University, Houston TX, USA. 03/2018 This document describes a synthetic benchmark data
More informationPreliminary Notions in Game Theory
Chapter 7 Preliminary Notions in Game Theory I assume that you recall the basic solution concepts, namely Nash Equilibrium, Bayesian Nash Equilibrium, Subgame-Perfect Equilibrium, and Perfect Bayesian
More informationCSEP 573: Artificial Intelligence
CSEP 573: Artificial Intelligence Markov Decision Processes (MDP)! Ali Farhadi Many slides over the course adapted from Luke Zettlemoyer, Dan Klein, Pieter Abbeel, Stuart Russell or Andrew Moore 1 Outline
More information2D5362 Machine Learning
2D5362 Machine Learning Reinforcement Learning MIT GALib Available at http://lancet.mit.edu/ga/ download galib245.tar.gz gunzip galib245.tar.gz tar xvf galib245.tar cd galib245 make or access my files
More informationTABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC
TABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC THOMAS BOLANDER AND TORBEN BRAÜNER Abstract. Hybrid logics are a principled generalization of both modal logics and description logics. It is well-known
More informationmonotone circuit value
monotone circuit value A monotone boolean circuit s output cannot change from true to false when one input changes from false to true. Monotone boolean circuits are hence less expressive than general circuits.
More informationPrice of Anarchy Smoothness Price of Stability. Price of Anarchy. Algorithmic Game Theory
Smoothness Price of Stability Algorithmic Game Theory Smoothness Price of Stability Recall Recall for Nash equilibria: Strategic game Γ, social cost cost(s) for every state s of Γ Consider Σ PNE as the
More informationCS 188 Fall Introduction to Artificial Intelligence Midterm 1. ˆ You have approximately 2 hours and 50 minutes.
CS 188 Fall 2013 Introduction to Artificial Intelligence Midterm 1 ˆ You have approximately 2 hours and 50 minutes. ˆ The exam is closed book, closed notes except your one-page crib sheet. ˆ Please use
More informationUncertainty Analysis with UNICORN
Uncertainty Analysis with UNICORN D.A.Ababei D.Kurowicka R.M.Cooke D.A.Ababei@ewi.tudelft.nl D.Kurowicka@ewi.tudelft.nl R.M.Cooke@ewi.tudelft.nl Delft Institute for Applied Mathematics Delft University
More informationPractical SAT Solving
Practical SAT Solving Lecture 1 Carsten Sinz, Tomáš Balyo April 18, 2016 NSTITUTE FOR THEORETICAL COMPUTER SCIENCE KIT University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz
More informationA Branch-and-Price method for the Multiple-depot Vehicle and Crew Scheduling Problem
A Branch-and-Price method for the Multiple-depot Vehicle and Crew Scheduling Problem SCIP Workshop 2018, Aachen Markó Horváth Tamás Kis Institute for Computer Science and Control Hungarian Academy of Sciences
More informationBusiness Process Management
Business Process Management Paolo Bottoni Lecture 5: AdvancedBPM Adapted from the slides for the book : Dumas, La Rosa, Mendling & Reijers: Fundamentals of Business Process Management, Springer 2013 http://courses.cs.ut.ee/2013/bpm/uploads/main/itlecture3.ppt
More information