Optimal Analyses for 3  n AB Games in the Worst Case Li-Te Huang and Shun-Shii Lin Dept. of Computer Science & Information Engineering, National Taiwan.

Slides:



Advertisements
Similar presentations
Chapter 11 Limitations of Algorithm Power Copyright © 2007 Pearson Addison-Wesley. All rights reserved.
Advertisements

Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 16 Relational Database Design Algorithms and Further Dependencies.
CPSC 504: Data Management Discussion on Chandra&Merlin 1977 Laks V.S. Lakshmanan Dept. of CS UBC.
Clustering Categorical Data The Case of Quran Verses
Copyright 2004 Koren & Krishna ECE655/DataRepl.1 Fall 2006 UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering Fault Tolerant Computing.
Techniques for Dealing with Hard Problems Backtrack: –Systematically enumerates all potential solutions by continually trying to extend a partial solution.
1 COMP 382: Reasoning about algorithms Unit 9: Undecidability [Slides adapted from Amos Israeli’s]
Calculus 30 C30.6 Demonstrate understanding of the application of derivatives to solve problems including: optimization rates of change related rates.
1 NP-Complete Problems. 2 We discuss some hard problems:  how hard? (computational complexity)  what makes them hard?  any solutions? Definitions 
Bounds on Code Length Theorem: Let l ∗ 1, l ∗ 2,..., l ∗ m be optimal codeword lengths for a source distribution p and a D-ary alphabet, and let L ∗ be.
Tutorial 6 of CSCI2110 Bipartite Matching Tutor: Zhou Hong ( 周宏 )
Introduction to Algorithms Jiafen Liu Sept
April 9, 2015Applied Discrete Mathematics Week 9: Relations 1 Solving Recurrence Relations Another Example: Give an explicit formula for the Fibonacci.
A (1+  )-Approximation Algorithm for 2-Line-Center P.K. Agarwal, C.M. Procopiuc, K.R. Varadarajan Computational Geometry 2003.
1 Introduction to Computability Theory Lecture12: Reductions Prof. Amos Israeli.
Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology.
1 Introduction to Computability Theory Lecture13: Mapping Reductions Prof. Amos Israeli.
Constraint Logic Programming Ryan Kinworthy. Overview Introduction Logic Programming LP as a constraint programming language Constraint Logic Programming.
1 Approximation Algorithms for Demand- Robust and Stochastic Min-Cut Problems Vineet Goyal Carnegie Mellon University Based on, [Golovin, G, Ravi] (STACS’06)
A general approximation technique for constrained forest problems Michael X. Goemans & David P. Williamson Presented by: Yonatan Elhanani & Yuval Cohen.
Polynomial time approximation scheme Lecture 17: Mar 13.
Rooks, Review  Objective : Students are able to explain cause and effect analysis paragraphs and comparison and contrast paragraph. Students.
Rooks, Review  Objective : Students are able to explain parts of paragraph and descriptive process analysis paragraphs Students are able to analyse.
1 Mesh Generation and Delaunay-Based Meshes Jernej Barbic Computer Science Department Carnegie Mellon University.
Applications of Symbolic Logic to Gene Regulation Systems Department of Computer Science and Information Engineering of National Chi-Nan University Advisor:
Domain Testing Based on Character String Predicate Ruilian Zhao Computer Science Dept. Beijing University of Chemical Technology Michael R. Lyu Computer.
Recursive Graph Deduction and Reachability Queries Yangjun Chen Dept. Applied Computer Science, University of Winnipeg 515 Portage Ave. Winnipeg, Manitoba,
Chapter 11 Limitations of Algorithm Power Copyright © 2007 Pearson Addison-Wesley. All rights reserved.
ECE 667 Synthesis and Verification of Digital Systems
The Marriage Problem Finding an Optimal Stopping Procedure.
1 Introduction to Approximation Algorithms. 2 NP-completeness Do your best then.
The Theory of NP-Completeness 1. What is NP-completeness? Consider the circuit satisfiability problem Difficult to answer the decision problem in polynomial.
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
Chapter 8. Section 8. 1 Section Summary Introduction Modeling with Recurrence Relations Fibonacci Numbers The Tower of Hanoi Counting Problems Algorithms.
SPANISH CRYPTOGRAPHY DAYS (SCD 2011) A Search Algorithm Based on Syndrome Computation to Get Efficient Shortened Cyclic Codes Correcting either Random.
INTEGRALS Areas and Distances INTEGRALS In this section, we will learn that: We get the same special type of limit in trying to find the area under.
Chapter 7 Optimization. Content Introduction One dimensional unconstrained Multidimensional unconstrained Example.
« Pruning Policies for Two-Tiered Inverted Index with Correctness Guarantee » Proceedings of the 30th annual international ACM SIGIR, Amsterdam 2007) A.
1 COMP3040 Tutorial 1 Analysis of algorithms. 2 Outline Motivation Analysis of algorithms Examples Practice questions.
Department of Electrical Engineering, Southern Taiwan University Robotic Interaction Learning Lab 1 The optimization of the application of fuzzy ant colony.
High-Speed Packet Classification Using Binary Search on Length Authors: Hyesook Lim and Ju Hyoung Mun Presenter: Yi-Sheng, Lin ( 林意勝 ) Date: Jan. 14, 2008.
Simple Iterative Sorting Sorting as a means to study data structures and algorithms Historical notes Swapping records Swapping pointers to records Description,
Protection vs. false targets in series systems Reliability Engineering and System Safety(2009) Kjell Hausken, Gregory Levitin Advisor: Frank,Yeong-Sung.
CSC 211 Data Structures Lecture 13
Expert Systems with Applications 34 (2008) 459–468 Multi-level fuzzy mining with multiple minimum supports Yeong-Chyi Lee, Tzung-Pei Hong, Tien-Chin Wang.
1 The Theory of NP-Completeness 2 Cook ’ s Theorem (1971) Prof. Cook Toronto U. Receiving Turing Award (1982) Discussing difficult problems: worst case.
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules S.D. Lee, David W. Cheung, Ben Kao The University of Hong.
Review 1 Arrays & Strings Array Array Elements Accessing array elements Declaring an array Initializing an array Two-dimensional Array Array of Structure.
Boolean Algebra and Computer Logic Mathematical Structures for Computer Science Chapter 7.1 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesBoolean Algebra.
Sorting and Searching. Selection Sort  “Search-and-Swap” algorithm 1) Find the smallest element in the array and exchange it with a[0], the first element.
Maximizing value and Minimizing base on Fuzzy TOPSIS model
LDK R Logics for Data and Knowledge Representation ClassL (Propositional Description Logic with Individuals) 1.
Network Flows Chun-Ta, Yu Graduate Institute Information Management Dept. National Taiwan University.
Dynamic Programming.  Decomposes a problem into a series of sub- problems  Builds up correct solutions to larger and larger sub- problems  Examples.
CS6045: Advanced Algorithms NP Completeness. NP-Completeness Some problems are intractable: as they grow large, we are unable to solve them in reasonable.
Monte Carlo Analysis of Uncertain Digital Circuits Houssain Kettani, Ph.D. Department of Computer Science Jackson State University Jackson, MS
A Different Solution  alternatively we can use the following algorithm: 1. if n == 0 done, otherwise I. print the string once II. print the string (n.
HANGMAN OPTIMIZATION Kyle Anderson, Sean Barton and Brandyn Deffinbaugh.
Adversarial Search 2 (Game Playing)
The Theory of NP-Completeness 1. Nondeterministic algorithms A nondeterminstic algorithm consists of phase 1: guessing phase 2: checking If the checking.
Chapter 13 Query Optimization Yonsei University 1 st Semester, 2015 Sanghyun Park.
Advanced Algorithms Analysis and Design By Dr. Nazir Ahmad Zafar Dr Nazir A. Zafar Advanced Algorithms Analysis and Design.
EE465: Introduction to Digital Image Processing
Scientific Research Group in Egypt (SRGE)
Classification of unlabeled data:
C ODEBREAKER Class discussion.
Analysis and design of algorithm
Chapter 11 Limitations of Algorithm Power
Danger Prediction by Case-Based Approach on Expressways
Presentation transcript:

Optimal Analyses for 3  n AB Games in the Worst Case Li-Te Huang and Shun-Shii Lin Dept. of Computer Science & Information Engineering, National Taiwan Normal University, Taipei, Taiwan, R.O.C

2 Outline Introduction The Key Idea of the Worst-Case Gaming Process Terminologies Analyses of the Worst-Case Strategy Conclusions

3 Introduction The deductive game is a kind of logic games. A codemaker and a codebreaker are involved. –Codemaker: think of a secret code in mind –Codebreaker: identify the secret code by guessing constantly. –The mission of the codebreaker: obtain the code minimize the number of guesses required –There are two families of deductive games according to one characteristic. Mastermind: repeated symbols are allowed in a code. AB game: all symbols within a code are distinct.

4 Introduction (cont.) 3  n AB game –A secret code: 3 digits and every digit has n possibilities (symbols) –For example, the set of these n symbols: S = {d 0,d 1,…,d n-1 } –The codemaker: c = d i d j d k, the codebreaker: g = d l d m d p

5 Introduction (cont.) The codemaker will give a response “xAyB” or called [x, y] as well. –x means the number of symbols which appear in both c and g and meanwhile, each symbol occupies the same position in both c and g. –y represents the number of symbols which occur in both c and g but the positions of these symbols in c and g do not match. –9 possible responses: [3, 0], [2, 0], [1, 2], [1, 1], [1, 0], [0, 3], [0, 2], [0, 1], and [0, 0]

6 The Key Idea of the Worst-Case Gaming Process d 0 d 1 d 2 1st guess d 3 d 4 d 5 2nd guess d 6 d 7 d 8 3rd guess Apply a branch-and-bound search algorithm to find an optimal strategy for smaller n. 1st response (the worst-case response) 0A0B 2nd response (the worst-case response) 0A0B 3rd response (the worst-case response) 0A0B When h  11 For a 3  20 AB game, the set of all symbols: S = {d 0, d 1, …, d 19 } 3  n AB game, n = 20 (best guess) Reduce to 3  h AB game, h = 17 Reduce to 3  h AB game, h = 14 Reduce to 3  h AB game, h = 11

7 An Illustrative Example for the Terminologies Ex: a 3  5 AB game –S = {0, 1, 2, 3, 4} –Codebreaker: g = 012 –Codemaker responses [2, 0], C [2,0] = {013, 014, 032, 042, 312, 412} –Another responses [1, 0], C [1,0] = {043, 034, 432, 342, 314, 413}

8 We say that C [1,0] dominates C [2,0] if we can find a mapping r – r can map from each code in C [2,0] to another one in C [1,0] –The mapped codes in C [1,0] preserve the structures of those in C [2,0]. Identifying a code in C [1,0] requires the same or larger number of guesses than that in C [2,0] if C [1,0] dominates C [2,0]. We call that C [1,0] is as hard as or harder than C [2,0]. C [2,0]  C [1,0]

9 Analyses of the Worst-Case Strategy Suppose that in a 3  n AB game, the set of symbols: S = { d 0,d 1,…,d n-1 } Assume that the first guess is d 0 d 1 d 2.

10 The Codemaker’s First Response The codemaker tries to maximize the number of guesses required for the codebreaker Among the 9 possible responses, the codemaker answers [0, 0], where n  11, at the first response. –We can prove that C [0,0] dominates the other 8 states by using the techniques of the structural reduction. –In other words, C [0,0] is the hardest state. C [0,0] C [0,1] C [1,0] C [0,2] C [1,1] C [2,0] C [0,3] C [1,2] C [3,0]

11 The Following Responses Now, our problem therefore reduces to a 3  h AB game, where h = n-3. In fact, for a 3  h AB game, where 11 ≤ h ≤ n –The codemaker always offers [0, 0] as the worst-case responses with the use of the same techniques to prove it.

12 Analyses of the Optimal Guesses for the Codebreaker in the following guesses The codebreaker has to determine the best guess when he encounters a 3  (n-3) AB game in the second turn. Define two subsets of S –A = {d 0, d 1, d 2 } –B = {d 3, d 4, d 5,… d n-1 }

13 Four Types of Guesses for the Codebreaker All possible guesses can be classified into four types –Suppose that d 0, d 1, d 2 ∈ A and d i, d j, d k ∈ B. Type 1: d 0 d 1 d 2 –If the codebreaker makes this kind of guesses, all the codes, which satisfy the first guess and first response, are then classified into the same substate trivially. –This will lead to non-optimal strategies.

14 Four Types of Guesses for the Codebreaker (cont.) Type 2: d 0 d 1 d i –With the use of the techniques of structural reductions, we can prove that C [0,0] dominates C [0,1] and C [1,0] –C [0,0] is produced when the codebreaker makes the second guess, d 0 d 1 d i, and the codemaker responses [0,0]. The meanings of the other two states are similar. C [0,0] C [0,1] C [1,0] When h  5

15 Four Types of Guesses for the Codebreaker (cont.) Type 3: d 0 d i d j –With taking advantage of the same techniques, we can also prove that C [0,0] dominates the other states. C [0,0] C [0,1] C [1,0] C [0,2] C [1,1] C [2,0] When h  8

16 Four Types of Guesses for the Codebreaker (cont.) Type 4: d i d j d k –We are able to prove that C [0,0] dominates other states as well. C [0,0] C [0,1] C [1,0] C [0,2] C [1,1] C [2,0] C [0,3] C [1,2] C [3,0] When h  11

17 Summaries of the Optimal Guesses for the Codebreaker Redefine C (2), C (3), and C (4) to denote the hardest states caused by guessing d 0 d 1 d i, d 0 d i d j, d i d j d k respectively. So, C (4) is the easiest state among the three state and then d i d j d k is the optimal guess for the codebreaker. C (2) C (3) C (4) Guess d 0 d 1 d i Guess d 0 d i d j Guess d i d j d k

18 Conclusions The codemaker and the codebreaker will have the above behavior until h  11. For a 3  n AB game, the minimum number of guesses for the codebreaker in the worst case can be derived as A natural generalization: m  n AB games, where m ≥ 4. This problem remains open.

Thanks for Your Attention.