The Computational Complexity of Some Games and Puzzles with Theoretical Applications
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Parity Games: Descriptive Complexity and Algorithms for New Solvers
Imperial College London Department of Computing Parity Games: Descriptive Complexity and Algorithms for New Solvers Huan-Pu Kuo 2013 Supervised by Prof Michael Huth, and Dr Nir Piterman Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Computing of Imperial College London and the Diploma of Imperial College London 1 Declaration I herewith certify that all material in this dissertation which is not my own work has been duly acknowledged. Selected results from this dissertation have been disseminated in scientific publications detailed in Chapter 1.4. Huan-Pu Kuo 2 Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. 3 Abstract Parity games are 2-person, 0-sum, graph-based, and determined games that form an important foundational concept in formal methods (see e.g., [Zie98]), and their exact computational complexity has been an open prob- lem for over twenty years now. In this thesis, we study algorithms that solve parity games in that they determine which nodes are won by which player, and where such decisions are supported with winning strategies. We modify and so improve a known algorithm but also propose new algorithmic approaches to solving parity games and to understanding their descriptive complexity. -
Words Should Be Fun: Scrabble As a Tool for Language Preservation in Tuvan and Other Local Languages1
Vol. 4 (2010), pp. 213-230 http://nflrc.hawaii.edu/ldc/ http://hdl.handle.net/10125/4480 Words should be fun: Scrabble as a tool for language preservation in Tuvan and other local languages1 Vitaly Voinov The University of Texas at Arlington One small but practical way of empowering speakers of an endangered language to maintain their language’s vitality amidst a climate of rapid globalization is to introduce a mother-tongue version of the popular word game Scrabble into their society. This paper examines how versions of Scrabble have been developed and used for this purpose in various endangered or non-prestige languages, with a focus on the Tuvan language of south Siberia, for which the author designed a Tuvan version of the game. Playing Scrab- ble in their mother tongue offers several benefits to speakers of an endangered language: it presents a communal approach to group literacy, promotes the use of a standardized orthography, creates new opportunities for intergenerational transmission of the language, expands its domains of usage, and may heighten the language’s external and internal prestige. Besides demonstrating the benefits of Scrabble, the paper also offers practical suggestions concerning both linguistic factors (e.g., choice of letters to be included, cal- culation of letter frequencies, dictionary availability) and non-linguistic factors (board de- sign, manufacturing, legal issues, etc.) relevant to producing Scrabble in other languages for the purpose of revitalization. 1. INTRODUCTION.2 The past several decades have seen globalization penetrating even the most remote corners of the world, bringing with them popular American exports such as Coca-Cola and Hollywood movies. -
Advanced Complexity Theory
Advanced Complexity Theory Markus Bl¨aser& Bodo Manthey Universit¨atdes Saarlandes Draft|February 9, 2011 and forever 2 1 Complexity of optimization prob- lems 1.1 Optimization problems The study of the complexity of solving optimization problems is an impor- tant practical aspect of complexity theory. A good textbook on this topic is the one by Ausiello et al. [ACG+99]. The book by Vazirani [Vaz01] is also recommend, but its focus is on the algorithmic side. Definition 1.1. An optimization problem P is a 4-tuple (IP ; SP ; mP ; goalP ) where ∗ 1. IP ⊆ f0; 1g is the set of valid instances of P , 2. SP is a function that assigns to each valid instance x the set of feasible ∗ 1 solutions SP (x) of x, which is a subset of f0; 1g . + 3. mP : f(x; y) j x 2 IP and y 2 SP (x)g ! N is the objective function or measure function. mP (x; y) is the objective value of the feasible solution y (with respect to x). 4. goalP 2 fmin; maxg specifies the type of the optimization problem. Either it is a minimization or a maximization problem. When the context is clear, we will drop the subscript P . Formally, an optimization problem is defined over the alphabet f0; 1g. But as usual, when we talk about concrete problems, we want to talk about graphs, nodes, weights, etc. In this case, we tacitly assume that we can always find suitable encodings of the objects we talk about. ∗ Given an instance x of the optimization problem P , we denote by SP (x) the set of all optimal solutions, that is, the set of all y 2 SP (x) such that mP (x; y) = goalfmP (x; z) j z 2 SP (x)g: (Note that the set of optimal solutions could be empty, since the maximum need not exist. -
Learning Board Game Rules from an Instruction Manual Chad Mills A
Learning Board Game Rules from an Instruction Manual Chad Mills A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science University of Washington 2013 Committee: Gina-Anne Levow Fei Xia Program Authorized to Offer Degree: Linguistics – Computational Linguistics ©Copyright 2013 Chad Mills University of Washington Abstract Learning Board Game Rules from an Instruction Manual Chad Mills Chair of the Supervisory Committee: Professor Gina-Anne Levow Department of Linguistics Board game rulebooks offer a convenient scenario for extracting a systematic logical structure from a passage of text since the mechanisms by which board game pieces interact must be fully specified in the rulebook and outside world knowledge is irrelevant to gameplay. A representation was proposed for representing a game’s rules with a tree structure of logically-connected rules, and this problem was shown to be one of a generalized class of problems in mapping text to a hierarchical, logical structure. Then a keyword-based entity- and relation-extraction system was proposed for mapping rulebook text into the corresponding logical representation, which achieved an f-measure of 11% with a high recall but very low precision, due in part to many statements in the rulebook offering strategic advice or elaboration and causing spurious rules to be proposed based on keyword matches. The keyword-based approach was compared to a machine learning approach, and the former dominated with nearly twenty times better precision at the same level of recall. This was due to the large number of rule classes to extract and the relatively small data set given this is a new problem area and all data had to be manually annotated. -
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model Kevin Sim Emma Hart Institute for Informatics and Digital Innovation Institute for Informatics and Digital Innovation Edinburgh Napier University Edinburgh Napier University Edinburgh, Scotland, UK Edinburgh, Scotland, UK [email protected] [email protected] ABSTRACT exhibit superior performance than similar human designed Novel deterministic heuristics are generated using Single Node heuristics and can be used to produce collections of heuris- Genetic Programming for application to the One Dimen- tics that collectively maximise the potential of selective HH. sional Bin Packing Problem. First a single deterministic The papers contribution is two fold. Single heuristics ca- heuristic was evolved that minimised the total number of pable of outperforming a range of well researched deter- bins used when applied to a set of 685 training instances. ministic heuristics are generated by combining features ex- Following this, a set of heuristics were evolved using a form tracted from those heuristics. Secondly an island model[19] of cooperative co-evolution that collectively minimise the is adapted to use multiple SNGP implementations to gener- number of bins used across the same set of problems. Re- ate diverse sets of heuristics which collectively outperform sults on an unseen test set comprising a further 685 prob- any of the single heuristics when used in isolation. Both ap- lem instances show that the single evolved heuristic out- proaches are trained and evaluated using equal divisions of a performs existing deterministic heuristics described in the large set of 1370 benchmark problem instances sourced from literature. -
APX Radio Family Brochure
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The Complexity Zoo
The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/. -
13. Mathematics University of Central Oklahoma
Southwestern Oklahoma State University SWOSU Digital Commons Oklahoma Research Day Abstracts 2013 Oklahoma Research Day Jan 10th, 12:00 AM 13. Mathematics University of Central Oklahoma Follow this and additional works at: https://dc.swosu.edu/ordabstracts Part of the Animal Sciences Commons, Biology Commons, Chemistry Commons, Computer Sciences Commons, Environmental Sciences Commons, Mathematics Commons, and the Physics Commons University of Central Oklahoma, "13. Mathematics" (2013). Oklahoma Research Day Abstracts. 12. https://dc.swosu.edu/ordabstracts/2013oklahomaresearchday/mathematicsandscience/12 This Event is brought to you for free and open access by the Oklahoma Research Day at SWOSU Digital Commons. It has been accepted for inclusion in Oklahoma Research Day Abstracts by an authorized administrator of SWOSU Digital Commons. An ADA compliant document is available upon request. For more information, please contact [email protected]. Abstracts from the 2013 Oklahoma Research Day Held at the University of Central Oklahoma 05. Mathematics and Science 13. Mathematics 05.13.01 A simplified proof of the Kantorovich theorem for solving equations using scalar telescopic series Ioannis Argyros, Cameron University The Kantorovich theorem is an important tool in Mathematical Analysis for solving nonlinear equations in abstract spaces by approximating a locally unique solution using the popular Newton-Kantorovich method.Many proofs have been given for this theorem using techniques such as the contraction mapping principle,majorizing sequences, recurrent functions and other techniques.These methods are rather long,complicated and not very easy to understand in general by undergraduate students.In the present paper we present a proof using simple telescopic series studied first in a Calculus II class. -
THE ART of Puzzle Game Design
THE ART OF Puzzle Design Scott Kim & Alexey Pajitnov with Bob Bates, Gary Rosenzweig, Michael Wyman March 8, 2000 Game Developers Conference These are presentation slides from an all-day tutorial given at the 2000 Game Developers Conference in San Jose, California (www.gdconf.com). After the conference, the slides will be available at www.scottkim.com. Puzzles Part of many games. Adventure, education, action, web But how do you create them? Puzzles are an important part of many computer games. Cartridge-based action puzzle gamse, CD-ROM puzzle anthologies, adventure game, and educational game all need good puzzles. Good News / Bad News Mental challenge Marketable? Nonviolent Dramatic? Easy to program Hard to invent? Growing market Small market? The good news is that puzzles appeal widely to both males and females of all ages. Although the market is small, it is rapidly expanding, as computers become a mass market commodity and the internet shifts computer games toward familiar, quick, easy-to-learn games. Outline MORNING AFTERNOON What is a puzzle? Guest Speakers Examples Exercise Case studies Question & Design process Answer We’ll start by discussing genres of puzzle games. We’ll study some classic puzzle games, and current projects. We’ll cover the eight steps of the puzzle design process. We’ll hear from guest speakers. Finally we’ll do hands-on projects, with time for question and answer. What is a Puzzle? Five ways of defining puzzle games First, let’s map out the basic genres of puzzle games. Scott Kim 1. Definition of “Puzzle” A puzzle is fun and has a right answer. -
Three-Dimensional Bin-Packing Approaches, Issues, and Solutions
Three Dimensional Bin-Packing Issues and Solutions Seth Sweep University of Minnesota, Morris [email protected] Abstract This paper reviews the three-dimensional version of the classic NP-hard bin-packing, optimization problem along with its theoretical relevance and practical importance. Also, new approximation solutions are introduced based on prior research. The three-dimensional bin-packing optimization problem concerns placing box-shaped objects of arbitrary size and number into a box-shaped, three-dimensional space efficiently. Approximation algorithms become a guide that attempts to place objects in the least amount of space and time. As a NP-hard problem, three-dimensional bin- packing holds much academic interest to computer scientists. However, this problem also has relevance in industrial settings such as shipping cargo and efficiently designing machinery with replaceable parts, such as automobiles. Industry relies on algorithms to provide good solutions to versions of this problem. This research project adapted common approaches to one-dimensional bin-packing, such as next fit and best fit, to three dimensions. Adaptation of these algorithms is possible by dividing the space of a bin. Then, by paying attention strictly to the width of each object, the one-dimensional approaches attempt to efficiently pack into the divided compartments of the original bin. Through focusing entirely on width, these divisions effectively become multiple one-dimensional bins. However, entirely disregarding the volume of the bin by ignoring height and depth may generate inefficient packings. As this paper details in more depth, generally cubical objects may pack well but exceptionally high or long objects leave large gaps of empty space unpacked. -
MODULES and ITS REVERSES 1. Introduction the Hölder Inequality
Ann. Funct. Anal. A nnals of F unctional A nalysis ISSN: 2008-8752 (electronic) URL: www.emis.de/journals/AFA/ HOLDER¨ TYPE INEQUALITIES ON HILBERT C∗-MODULES AND ITS REVERSES YUKI SEO1 This paper is dedicated to Professor Tsuyoshi Ando Abstract. In this paper, we show Hilbert C∗-module versions of H¨older- McCarthy inequality and its complementary inequality. As an application, we obtain H¨oldertype inequalities and its reverses on a Hilbert C∗-module. 1. Introduction The H¨olderinequality is one of the most important inequalities in functional analysis. If a = (a1; : : : ; an) and b = (b1; : : : ; bn) are n-tuples of nonnegative numbers, and 1=p + 1=q = 1, then ! ! Xn Xn 1=p Xn 1=q ≤ p q aibi ai bi for all p > 1 i=1 i=1 i=1 and ! ! Xn Xn 1=p Xn 1=q ≥ p q aibi ai bi for all p < 0 or 0 < p < 1: i=1 i=1 i=1 Non-commutative versions of the H¨olderinequality and its reverses have been studied by many authors. T. Ando [1] showed the Hadamard product version of a H¨oldertype. T. Ando and F. Hiai [2] discussed the norm H¨olderinequality and the matrix H¨olderinequality. B. Mond and O. Shisha [15], M. Fujii, S. Izumino, R. Nakamoto and Y. Seo [7], and S. Izumino and M. Tominaga [11] considered the vector state version of a H¨oldertype and its reverses. J.-C. Bourin, E.-Y. Lee, M. Fujii and Y. Seo [3] showed the geometric operator mean version, and so on. -
Can You Change MAN to APE? Computers Can
Can a Computer Solve a Word Puzzle? - or - Can You Change MAN to APE? Computers can add; they can store numbers; they can solve equations. But word puzzles ask for abilities we don't usually associate with computers. Can we apply computational thinking to these unusual problems? To do so, we have to understand how we solve these puzzles, and identify those parts of our thought processes that can be \explained" to a computer. Computational Thinking In this discussion, we will look at a simple word puzzle. If we think about how we solve such puzzles, we can identify some mental pro- cesses (human thinking) that a computer would have to mimic: • memory: we need to know a lot of words; • imagination: we need to imagine possible changes to a word; • evaluation: given several possible changes, we need to choose the one most likely to take us to our goal; • backtracking: when a choice doesn't work out, we need to backtrack and search for an alternate choice; If we want a computer to solve these puzzles, we have to understand how we do them first, and then try to translate our thinking into computational thinking. DOUBLETS: Invented by Lewis Carroll DOUBLETS: Invented by Lewis Carroll Lewis Carroll, who wrote the children's book \Alice in Wonderland", was very fond of word games and puzzles. He asked a riddle that no one has solved: Why is a raven like a writing desk?. He wrote poems like Jabberwocky full of nonsense words, a few of which were absorbed into English: burbled and gallumphing.