Relevant bibliographies by topics / Game; Memory; Algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Game; Memory; Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Game; Memory; Algorithms"

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Bosansky, B., C. Kiekintveld, V. Lisy, and M. Pechoucek. "An Exact Double-Oracle Algorithm for Zero-Sum Extensive-Form Games with Imperfect Information." Journal of Artificial Intelligence Research 51 (December 30, 2014): 829–66. http://dx.doi.org/10.1613/jair.4477.

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Developing scalable solution algorithms is one of the central problems in computational game theory. We present an iterative algorithm for computing an exact Nash equilibrium for two-player zero-sum extensive-form games with imperfect information. Our approach combines two key elements: (1) the compact sequence-form representation of extensive-form games and (2) the algorithmic framework of double-oracle methods. The main idea of our algorithm is to restrict the game by allowing the players to play only selected sequences of available actions. After solving the restricted game, new sequences are added by finding best responses to the current solution using fast algorithms. We experimentally evaluate our algorithm on a set of games inspired by patrolling scenarios, board, and card games. The results show significant runtime improvements in games admitting an equilibrium with small support, and substantial improvement in memory use even on games with large support. The improvement in memory use is particularly important because it allows our algorithm to solve much larger game instances than existing linear programming methods. Our main contributions include (1) a generic sequence-form double-oracle algorithm for solving zero-sum extensive-form games; (2) fast methods for maintaining a valid restricted game model when adding new sequences; (3) a search algorithm and pruning methods for computing best-response sequences; (4) theoretical guarantees about the convergence of the algorithm to a Nash equilibrium; (5) experimental analysis of our algorithm on several games, including an approximate version of the algorithm.
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Bulitko, V., Y. Björnsson, and R. Lawrence. "Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding." Journal of Artificial Intelligence Research 39 (September 29, 2010): 269–300. http://dx.doi.org/10.1613/jair.3076.

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Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.
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Hasanudin, Muhaimin, Turki Salim, and Arkan Nur Robby. "RANCANG BANGUN APLIKASI GAME PUZZLE BERBASIS ANDROID MENGGUNAKAN ALGORITMA DECISION TREE." Journal CERITA 3, no. 2 (August 1, 2017): 171–80. http://dx.doi.org/10.33050/cerita.v3i2.654.

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The development of games today is very rapid and has become part of the lifestyle. A puzzle game is an application that can be used to hone thinking and memory skills where users have to find a way out and collect special items that have been provided at every level of the game . The player must collect all the white orb in each level to get the key to move to the next level but the player will be confronted by the constraints on how to find the key and retrieve it . The game is designed using decision tree and game algorithms designed using Game Maker Studio that is applied to Android devices.
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Pratiwi, Heny, Ita Arfyanti, and M. Za’iem Sururi. "Membangun Game Edutainment “Pengenalan Komputer” Menggunakan Shuffle Random (SR) Dan Finite State Machine (FSM) Untuk Anak Tunagrahita Ringan." J-SAKTI (Jurnal Sains Komputer dan Informatika) 3, no. 2 (September 13, 2019): 307. http://dx.doi.org/10.30645/j-sakti.v3i2.149.

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Research to build an Edutainment Game "Introduction to Computers" for mild retarded children is a study designed for teachers to deliver material to special school students (SLB) with special needs such as children with visual impairment, hearing impairment, mental retardation, intellectual disability, autism and blindness. . In this game the player will be presented with a variety of learning menus about "Computer Devices" and a game menu consisting of a guessing picture quiz to train memory, then there are typing games and labyrinth games to develop motor movements and increase students' responses and interests. Shuffle and Finite State Machine randomization algorithms will be applied in this study, with the aim of arranging the image position to be randomized in the learning menu and typing games as well as randomizing the position of the main characters and enemies in the labyrinth game made in three levels. This is done to improve students' learning and curiosity. The technology applied in this research is intelligent agent (intelligent system) which has a game agent character that will accompany children to learn and play.
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Yun, Heuijee, and Daejin Park. "Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle." Electronics 10, no. 17 (August 30, 2021): 2102. http://dx.doi.org/10.3390/electronics10172102.

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Computer simulation based on digital twin is an essential process when designing self-driving cars. However, designing a simulation program that is exactly equivalent to real phenomena can be arduous and cost-ineffective because too many things must be implemented. In this paper, we propose the method using the online game GTA5 (Grand Theft Auto5), as a groundwork for autonomous vehicle simulation. As GTA5 has a variety of well-implemented objects, people, and roads, it can be considered a suitable tool for simulation. By using OpenCV (Open source computer vision) to capture the GTA5 game screen and analyzing images with YOLO (You Only Look Once) and TensorFlow based on Python, we can build a quite accurate object recognition system. This can lead to writing of algorithms for object avoidance and lane recognition. Once these algorithms have been completed, vehicles in GTA5 can be controlled through codes composed of the basic functions of autonomous driving, such as collision avoidance and lane-departure prevention. In addition, the algorithm tested with GTA5 has been implemented with a programmable RC car (Radio control car), DonkeyCar, to increase reliability. By testing those algorithms, we can ensure that the algorithms can be conducted in real time and they cost low power and low memory size. Therefore, we have found a way to approach digital twin technology one step more easily.
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Kitakoshi, Daisuke, Ryo Hanada, Keitarou Iwata, and Masato Suzuki. "Cognitive Training System for Dementia Prevention Using Memory Game Based on the Concept of Human-Agent Interaction." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 727–37. http://dx.doi.org/10.20965/jaciii.2015.p0727.

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This paper describes a cognitive training system to help older adults to stimulate and maintain their cognitive functions based on a memory game implemented on a tablet device. In this system, a software agent incorporated into the tablet device performs dialogic interactions with users based on the concept of human-agent interaction (HAI) to (i) reduce their psychological resistance to the system; (ii) maintain their interest in the game; and (iii) improve the motivation for users to play the game long term. The difficulty level of the game is adjusted through reinforcement learning algorithms depending on the proficiency of respective users. Several experiments and subjective evaluations by older adults were conducted to evaluate the basic characteristics of the system, and to investigate the impact of the system on cognitive function. The ultimate goal of the proposed system is to establish an environment in which users can continuously engage in dementia-prevention activities without getting bored.
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Fortuna RGP, Putrie, and Ahmaddul Hadi. "RANCANG BANGUN APLIKASI BELAJAR PEMROGRAMAN DENGAN GAME EDUCATION PADA SMARTPHONE BERBASIS ANDROID." Voteteknika (Vocational Teknik Elektronika dan Informatika) 7, no. 3 (July 10, 2019): 30. http://dx.doi.org/10.24036/voteteknika.v7i3.105086.

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Programming is a vocational subject in the Vocational School majoring in Software Engineering and Electronics. Programming is classified as a subject that is less desirable and difficult for students, because these subjects are not graphically based and students do not understand the basic structure of the program. And less Affective use of conventional learning resources as the main source of the learning. With the existence of smartphones can be used as an additional source in the world of education. One of them is the use of smartphones as learning media. The purpose of this study is to produce learning media that can help teachers and students in the learning process by loading educational games that can attract students' interest in learning. This application is made using the System Development Life Cycle (SDLC) Prototype method, using an Android-based java programming language, by applying game education algorithms and word game genres where players must arrange random letters into coherent sentences, the random letters will be matched with the answers stored in the database to be the correct answer. The content in the application is the 2013 curriculum syllabus. The material consists of RPP and teaching materials for each basic competency. Ebooks, articles and videos that discuss basic programming. Evaluation is a practice question to measure the level of understanding of students and quiz games in the form of stacking words to train students' memory about basic programming.Keywords: Android, Programming, Game education. word game
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Kim, Sungwook. "Cooperative Game-Based Virtual Machine Resource Allocation Algorithms in Cloud Data Centers." Mobile Information Systems 2020 (March 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/9840198.

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With the growing demand of cloud services, cloud data centers (CDCs) can provide flexible resource provisioning in order to accommodate the workload demand. In CDCs, the virtual machine (VM) resource allocation problem is an important and challenging issue to provide efficient infrastructure services. In this paper, we propose a unified resource allocation scheme for VMs in the CDC system. To provide a fair-efficient solution, we concentrate on the basic concept of Shapley value and adopt its variations to effectively allocate CDC resources. Based on the characteristics of value solutions, we develop novel CPU, memory, storage, and bandwidth resource allocation algorithms. To practically implement our algorithms, application types are assumed as cooperative game players, and different value solutions are applied to optimize the resource utilization. Therefore, our four resource allocation algorithms are jointly combined as a novel fourfold game model and take various benefits in a rational way through the cascade interactions while solving comprehensively some control issues. To ensure the growing demand of cloud services, this feature can leverage the full synergy of different value solutions. To check the effectiveness and superiority of our proposed scheme, we conduct extensive simulations. The simulation results show that our algorithms have significant performance improvement compared to the existing state-of-the-art protocols. Finally, we summarize our cooperative game-based approach and discuss possible major research issues for the future challenges about the cloud-assisted DC resource allocation paradigm.
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Kodynets, Anatolii, and Arsen Murashko. "Video game in the system of intellectual property: the concept and features of legal protection." Theory and Practice of Intellectual Property, no. 3 (September 7, 2021): 57–66. http://dx.doi.org/10.33731/32021.239584.

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Keywords: video game, intellectual property law, computer program, software, digitalinformation, electronic information, multimedia work, audiovisual work The article is devoted to thestudy of the legal understanding of video games as an object of intellectual propertyrights. The author concludes that video games constitute digital information, whichgreatly facilitates their development, however, complicates the protection of intellectualproperty rights. There is a contradictory connection between the concepts of«electronic (digital) information», «audiovisual work», «computer program» and «literarywork», which establishes some uncertainty in the protection of video games,namely, what should be understood by these concepts in terms of law.The author found that the use of the latest technologies to improve the visual componentbrings the video game even closer to the game audiovisual object, which increasesthe complexity of the process of protection of intellectual property rights. However, thecurrent situation with the use of the term «computer program» in the context of protectionof intellectual property rights to video games does not cover all features of the latter,covering only the main program among the software recording and operation of thegame, such as audiovisual content. Therefore, there is a need to use the generalized conceptof «software» as opposed to the narrower concept of «computer program». In thiscase, in the absence of at least one of the characteristics of the multimedia work, the computerprogram should be referred to the scope of legal regulation of software.The author proposed to understand the video game as object of intellectual propertylaw in the following manner: multimedia work expressed in the form of digital(electronic) information, the principle of which is determined by the algorithms of thesoftware provided for installation in the memory of computer devices.In general, the digital nature of video games presupposes a revision of existing approachesto the protection of intellectual property rights, which may be aimed atweakening the regulatory function of the law where it is possible to regulate the relationshipby more flexible means.
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Gallardo, J. E., C. Cotta, and A. J. Fernández. "Solving Weighted Constraint Satisfaction Problems with Memetic/Exact Hybrid Algorithms." Journal of Artificial Intelligence Research 35 (July 28, 2009): 533–55. http://dx.doi.org/10.1613/jair.2770.

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A weighted constraint satisfaction problem (WCSP) is a constraint satisfaction problem in which preferences among solutions can be expressed. Bucket elimination is a complete technique commonly used to solve this kind of constraint satisfaction problem. When the memory required to apply bucket elimination is too high, a heuristic method based on it (denominated mini-buckets) can be used to calculate bounds for the optimal solution. Nevertheless, the curse of dimensionality makes these techniques impractical on large scale problems. In response to this situation, we present a memetic algorithm for WCSPs in which bucket elimination is used as a mechanism for recombining solutions, providing the best possible child from the parental set. Subsequently, a multi-level model in which this exact/metaheuristic hybrid is further hybridized with branch-and-bound techniques and mini-buckets is studied. As a case study, we have applied these algorithms to the resolution of the maximum density still life problem, a hard constraint optimization problem based on Conway's game of life. The resulting algorithm consistently finds optimal patterns for up to date solved instances in less time than current approaches. Moreover, it is shown that this proposal provides new best known solutions for very large instances.
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Dissertations / Theses on the topic "Game; Memory; Algorithms"

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Smith, Justin N. "Computational complexity, bounded rationality and the theory of games." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365642.

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Cotton-Barratt, Conrad. "Using class memory automata in algorithmic game semantics." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:f270020c-9d74-4981-9a64-bba1c8b0343c.

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Automata over infinite alphabets are a powerful extension of traditional automata theory, which have begun to be used in solving problems in verification and databases. The infinite nature of the alphabet allows the automata to model potentially unbounded processes or structures at a higher level of precision than finite alphabet automata. Game semantics provides a powerful framework for ascribing fully abstract semantics to higher-order programs, by modelling programs as strategies for interaction between the program and the environment. Questions about properties of programs can then be reduced to checking properties of these strategies. This thesis aims to continue the classification of the fragments of the higher-order call-by-value language RML for which observational equivalence is decidable. To do so, we will develop new variants of class memory automata that allow us to express and compare languages representing game semantic strategies. We begin our contributions by looking at class memory automata, a natural form of automata over infinite alphabets. We identify a natural restriction - which we call weakness - that leads to better algorithmic and complexity properties. We then admit a tree-structure to the infinite alphabets being used, and present a decidable form of class memory automata over these structured inputs. We also introduce and study a form of automaton combining the class memory operation over infinite alphabets with a visibly pushdown stack. Next, we use these form of class memory automata in deciding observational equivalence of RML programs. RML can be viewed as the restricton of Standard ML to ground-type references, with a 'bad variable' construct. Using the game semantics of RML, we reduce observational equivalence to equivalence of these variants of class memory automata. These methods provide new results on which fragments of RML have decidable observational equivalence. We also present several new undecidability and results, showing fragments of RML to have an undecidable observational equivalence problem.
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Wilton, Steven J. E. "Architectures and algorithms for field-programmable gate arrays with embedded memory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ28082.pdf.

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Chandrakar, Shant. "Memory Architecture Template for Fast Block Matching Algorithms on Field Programmable Gate Arrays." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/495.

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Fast Block Matching (FBM) algorithms for video compression are well suited for acceleration using parallel data-path architectures on Field Programmable Gate Arrays (FPGAs). However, designing an efficient on-chip memory subsystem to provide the required throughput to this parallel data-path architecture is a complex problem. This thesis presents a memory architecture template that can be parameterized for a given FBM algorithm, number of parallel Processing Elements (PEs), and block size. The template can be parameterized with well known exploration techniques to design efficient on-chip memory subsystems. The memory subsystems are derived for two existing FBM algorithms and are implemented on a Xilinx Virtex 4 family of FPGAs. Results show that the derived memory subsystem in the best case supports up to 27 more parallel PEs than the three existing subsystems and processes integer pixels in a 1080p video sequence up to a rate of 73 frames per second. The speculative execution of an FBM algorithm for the same number of PEs increases the number of frames processed per second by 49%.
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Meenakshi, Siddharthan Rathna Keerthi. "Fault Modeling and Analysis for FinFET SRAM Arrays." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368014414.

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Book chapters on the topic "Game; Memory; Algorithms"

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Wu, Ping-hsun, Ping-Yi Liu, and Tsan-sheng Hsu. "An External-Memory Retrograde Analysis Algorithm." In Computers and Games, 145–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11674399_10.

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Novak, Damijan, and Domen Verber. "New Generation of Artificial Intelligence for Real-Time Strategy Games." In Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability, 220–29. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4490-8.ch021.

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Artificial intelligence in computer games is still well behind academic artificial intelligence research. The computer power and memory resources have increased exponentially over the last few years and improved game artificial intelligence should not hinder the performance of the game anymore. Improvements of game artificial intelligence are necessary because an appropriate artificial intelligence for the more advanced players does not exist today. This chapter discusses artificial intelligence for real-time strategy computer games, which are ideal test beds for research on movement, tactic, and strategy. Open-source real-time strategy game development tools are presented and compared, and an enhanced combat artificial intelligence algorithm is proposed.
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Ang, Li-Minn, Kah Phooi Seng, and Christopher Wing Hong Ngau. "Biologically Inspired Components in Embedded Vision Systems." In Computer Vision, 458–93. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch018.

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Biological vision components like visual attention (VA) algorithms aim to mimic the mechanism of the human vision system. Often VA algorithms are complex and require high computational and memory requirements to be realized. In biologically-inspired vision and embedded systems, the computational capacity and memory resources are of a primary concern. This paper presents a discussion for implementing VA algorithms in embedded vision systems in a resource constrained environment. The authors survey various types of VA algorithms and identify potential techniques which can be implemented in embedded vision systems. Then, they propose a low complexity and low memory VA model based on a well-established mainstream VA model. The proposed model addresses critical factors in terms of algorithm complexity, memory requirements, computational speed, and salience prediction performance to ensure the reliability of the VA in a resource constrained environment. Finally a custom softcore microprocessor-based hardware implementation on a Field-Programmable Gate Array (FPGA) is used to verify the implementation feasibility of the presented model.
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Michalek, Anne M. P., Gavindya Jayawardena, and Sampath Jayarathna. "Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity." In Computational Models for Biomedical Reasoning and Problem Solving, 66–88. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7467-5.ch003.

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ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.
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Wu, Fan, Emmanuel Agu, Clifford Lindsay, and Chung-han Chen. "UbiWave." In Handheld Computing for Mobile Commerce, 124–79. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-761-9.ch008.

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Advances in ubiquitous displays and wireless communications have fueled the emergence of exciting mobile graphics applications including 3D virtual product catalogs, 3D maps, security monitoring systems and mobile games. Current trends that use cameras to capture geometry, material re?ectance and other graphics elements mean that very high resolution inputs are accessible to render extremely photorealistic scenes. However, captured graphics content can be many gigabytes in size, and must be simpli?ed before they can be used on small mobile devices, which have limited resources, such as memory, screen size and battery energy. Scaling and converting graphics content to a suitable rendering format involves running several software tools, and selecting the best resolution for target mobile device is often done by trial and error, which all takes time. Wireless errors can also affect transmitted content and aggressive compression is needed for low-bandwidth wireless networks. Most rendering algorithms are currently optimized for visual realism and speed, but are not resource or energy ef?cient on a mobile device. This chapter focuses on the improvement of rendering performance by reducing the impacts of these problems with UbiWave, an end-to-end framework to enable real time mobile access to high resolution graphics using wavelets. The framework tackles the issues including simpli?cation, transmission, and resource ef?cient rendering of graphics content on mobile device based on wavelets by utilizing 1) a Perceptual Error Metric (PoI) for automatically computing the best resolution of graphics content for a given mobile display to eliminate guesswork and save resources, 2) Unequal Error Protection (UEP) to improve the resilience to wireless errors, 3) an Energy-ef?cient Adaptive Real-time Rendering (EARR) heuristic to balance energy consumption, rendering speed and image quality and 4) an Energy-ef?cient Streaming Technique. The results facilitate a new class of mobile graphics application which can gracefully adapt the lowest acceptable rendering resolution to the wireless network conditions and the availability of resources and battery energy on mobile device adaptively.
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Conference papers on the topic "Game; Memory; Algorithms"

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Čermák, Jiří, Viliam Lisý, and Branislav Bošanský. "Automated Construction of Bounded-Loss Imperfect-Recall Abstractions in Extensive-Form Games (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/701.

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Information abstraction is one of the methods for tackling large extensive-form games (EFGs). Removing some information available to players reduces the memory required for computing and storing strategies. We present novel domain-independent abstraction methods for creating very coarse abstractions of EFGs that still compute strategies that are (near) optimal in the original game. First, the methods start with an arbitrary abstraction of the original game (domain-specific or the coarsest possible). Next, they iteratively detect which information is required in the abstract game so that a (near) optimal strategy in the original game can be found and include this information into the abstract game. Moreover, the methods are able to exploit imperfect-recall abstractions where players can even forget the history of their own actions. We present two algorithms that follow these steps -- FPIRA, based on fictitious play, and CFR+IRA, based on counterfactual regret minimization. The experimental evaluation confirms that our methods can closely approximate Nash equilibrium of large games using abstraction with only 0.9% of information sets of the original game.
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Tsompanas, Michail-Antisthenis I., Christoforos Kachris, and Georgios Ch Sirakoulis. "Optimization of Shared-Memory Multicore Systems Using Game Theory and Genetic Algorithms on Cellular Automata Lattices." In 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2013. http://dx.doi.org/10.1109/ipdpsw.2013.233.

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Cermak, Jiri, Branislav Bošanský, and Viliam Lisý. "An Algorithm for Constructing and Solving Imperfect Recall Abstractions of Large Extensive-Form Games." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/130.

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We solve large two-player zero-sum extensive-form games with perfect recall. We propose a new algorithm based on fictitious play that significantly reduces memory requirements for storing average strategies. The key feature is exploiting imperfect recall abstractions while preserving the convergence rate and guarantees of fictitious play applied directly to the perfect recall game. The algorithm creates a coarse imperfect recall abstraction of the perfect recall game and automatically refines its information set structure only where the imperfect recall might cause problems. Experimental evaluation shows that our novel algorithm is able to solve a simplified poker game with 7.10^5 information sets using an abstracted game with only 1.8% of information sets of the original game. Additional experiments on poker and randomly generated games suggest that the relative size of the abstraction decreases as the size of the solved games increases.
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Lin, Zichuan, Tianqi Zhao, Guangwen Yang, and Lintao Zhang. "Episodic Memory Deep Q-Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/337.

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Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interactions with the environments to obtain satisfactory performances. Recently, episodic memory based RL has attracted attention due to its ability to latch on good actions quickly. In this paper, we present a simple yet effective biologically inspired RL algorithm called Episodic Memory Deep Q-Networks (EMDQN), which leverages episodic memory to supervise an agent during training. Experiments show that our proposed method leads to better sample efficiency and is more likely to find good policy. It only requires 1/5 of the interactions of DQN to achieve many state-of-the-art performances on Atari games, significantly outperforming regular DQN and other episodic memory based RL algorithms.
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Chatterjee, Krishnendu, and Rasmus Ibsen-Jensen. "The Value 1 Problem Under Finite-memory Strategies for Concurrent Mean-payoff Games." In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973730.69.

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Hoffmann, M., C. Nowak, A. Haase, and S. Eckl. "Characterizing Gate Disturb Embedded Flash Memory Cells by Atomic Force Probing." In ISTFA 2010. ASM International, 2010. http://dx.doi.org/10.31399/asm.cp.istfa2010p0398.

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Abstract There are two known failure mechanisms that cause Gate Disturb failures in flash devices. One main electrically classified failure is the Gate Disturb failure. A second mechanism is the Floating Gate charging caused by high energetic electrons, so-called channel hot electrons, jumping above the energetic barrier of tunnel oxide. This paper describes the characterization of a single transistor Flash cell with the nano-probing approach and introduces a test algorithm to distinguish between these mechanisms at a Gate Disturb affected Flash cell. A Keithley parameter analyzer in combination with Atomic Force Probing (AFP) has been used for the Flash cell device characterization. A Gate Disturb defect can be induced by different defect mechanisms. Two of them, TRAP's in tunnel oxide and channel hot electrons as a result of leaky PN-junctions, were identified as the main root causes. These mechanisms can be distinguished by AFP-analysis with the tests presented in the paper.
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Chen, Jie, and Xiang Li. "A Minimal Memory Game-Based Distributed Algorithm to Vertex Cover of Networks." In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021. http://dx.doi.org/10.1109/iscas51556.2021.9401117.

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Luo, Donghao, Bingbing Ni, Yichao Yan, and Xiaokang Yang. "Image Matching via Loopy RNN." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/335.

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Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i.e., image matching, by recursively looking at specific/related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.
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Liang, Junchi, and Abdeslam Boularias. "Inferring Time-delayed Causal Relations in POMDPs from the Principle of Independence of Cause and Mechanism." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/268.

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This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at regular or arbitrary times, with the objective of improving data-efficiency and interpretability of model-based reinforcement learning (RL) techniques. The proposed algorithm initially predicts observations with the Markov assumption, and incrementally introduces new hidden variables to explain and reduce the stochasticity of the observations. The hidden variables are memory units that keep track of pertinent past events. Such events are systematically identified by their information gains. A test of independence between inputs and mechanisms is performed to identify cases when there is a causal link between events and those when the information gain is due to confounding variables. The learned transition and reward models are then used in a Monte Carlo tree search for planning. Experiments on simulated and real robotic tasks, and the challenging 3D game Doom show that this method significantly improves over current RL techniques.
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Wang, Yuxin, Peng Li, and Jason Cong. "Theory and algorithm for generalized memory partitioning in high-level synthesis." In FPGA'14: The 2014 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2554688.2554780.

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