A Novel Evolutionary Algorithm with Column and Sub-Block Local Search for Sudoku Puzzles

Sudoku puzzles are not only popular intellectual games but also NP-hard combinatorial problems related to various real-world applications, which have attracted much attention worldwide. Although many efficient tools, such as evolutionary computation (EC) algorithms, have been proposed for solving Sudoku puzzles, they still face great challenges with regard to hard and large instances of Sudoku puzzles. Therefore, to efficiently solve Sudoku puzzles, this paper proposes a genetic algorithm (GA)-based method with a novel local search technology called local search-based GA (LSGA). The LSGA includes three novel design aspects. First, it adopts a matrix coding scheme to represent individuals and designs the corresponding crossover and mutation operations. Second, a novel local search strategy based on column search and sub-block search is proposed to increase the convergence speed of the GA. Third, an elite population learning mechanism is proposed to let the population evolve by learning the historical optimal solution. Based on the above technologies, LSGA can greatly improve the search ability for solving complex Sudoku puzzles. LSGA is compared with some state-of-the-art algorithms at Sudoku puzzles of different difficulty levels and the results show that LSGA performs well in terms of both convergence speed and success rates on the tested Sudoku puzzle instances

Probabilistic Modeling for Game Content Creation and Adaption

Dynamic Difficulty Adjustment studies how games can adapt content totheir users’ skill level, aiming to keep them in flow. Most of these methodsmaximize engagement or minimize churn by adapting factors like the opponentAI or the availability of resources. However, such methods do notmaintain a model of the player, and use technologies that are highly specificto the games in which they are tested (e.g. requiring forward modelsfor enemy AIs based on planning agents). Designers may also intend tofind content that is more difficult/easier on purpose, and current methodsdo not allow for such targeting.This thesis proposes and tests a framework for adapting game content tousers based on Bayesian Optimization, giving designers flexibility whenchoosing which skill level to target. Starting with a design space, a metricto be measured, a prior over this metric, and a target value, our frameworkquickly searches possible levels/tasks for one with ideal difficulty (i.e. closeto the specified target). In the process, our framework maintains a simpledata-driven model of the player, which could be used for further decisionmakingand analysis.We test this framework in two settings: adapting content to planning agentsbased on search algorithms likeMonte Carlo Tree Search and Rolling HorizonEvolution in a dungeon crawler-type game, and adapting both Sudokupuzzles and dungeon crawler levels to players. Our framework successfullyadapts content to planning agents as long as their skill level is not extreme,and takes roughly 7 iterations to find an appropriate Sudoku puzzle.Additionally, instead of relying on designers to specify a real-valued encodingof the content (e.g. the number of pre-filled cells in a Sudoku puzzle),we investigate learning this encoding automatically usingDeep GenerativeModels. In other words, we explore design spaces learned as latent spacesof Variational Autoencoders using tile-based representations of games likeSuperMario Bros and The Legend of Zelda.Our final contribution is a novel way of interpolating, sampling and optimizingin the playable regions of latent spaces of Variational Autoencoders,and addresses the challenge that generative models are not always guaranteedto decode playable content. This contribution, based on differentialgeometry, is inspired by recent advancements in domains like robotics andproteinmodeling. We combine these ideas of safe generation with contentoptimization and propose a restricted version of Bayesian Optimization,which optimizes content inside playable regions. We see a clear trade-off:restricting the latent space to playable regions decreases the diversity ofthe generated content, as well as the quality of the optimal values in theoptimization.In summary, this thesis studies applications of Bayesian Optimization andDeep Generative Models to the problem of creating and adapting gamecontent to users. We develop a framework that quickly finds relevant levelsin settings varying from corpora of levels to the latent spaces of generativemodels, and we show in experiments involving both human and artificialplayers that this framework finds appropriate game content in a few iterations.This framework is readily applicable, and could be used to creategames that learn and adapt to their players.<br/

Autogenerative Networks

Artificial intelligence powered by deep neural networks has seen tremendous improvements in the last decade, achieving superhuman performance on a diverse range of tasks. Many worry that it can one day develop the ability to recursively self-improve itself, leading to an intelligence explosion known as the Singularity. Autogenerative networks, or neural networks generating neural networks, is one major plausible pathway towards realizing this possibility. The object of this thesis is to study various challenges and applications of small-scale autogenerative networks in domains such as artificial life, reinforcement learning, neural network initialization and optimization, gradient-based meta-learning, and logical networks. Chapters 2 and 3 describe novel mechanisms for generating neural network weights and embeddings. Chapters 4 and 5 identify problems and propose solutions to fix optimization difficulties in differentiable mechanisms of neural network generation known as Hypernetworks. Chapters 6 and 7 study implicit models of network generation like backpropagating through gradient descent itself and integrating discrete solvers into continuous functions. Together, the chapters in this thesiscontribute novel proposals for non-differentiable neural network generation mechanisms, significant improvements to existing differentiable network generation mechanisms, and an assimilation of different learning paradigms in autogenerative networks

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Cognitive Aging

The study of cognitive function in gerontology is considered relevant because it is an important risk factor for other pathologies in the old age, such as physical disability and dependence, depression, and frailty, mainly because of early pathological changes in cognitive function which are considered a preclinical state that may progress to dementia. In this chapter, cognitive functioning and the dimensions that are included in it (attention, memory, meta-memory, processing speed, executive functions, visuospatial skills, and language) are conceptualized. Additionally, the current evidence is analyzed regarding age-associated changes that are experienced during cognitive aging. These changes, or cognitive decline, are distinguished from those that are part of cognitive pathologies, the most common mild cognitive impairment and dementia. Such pathologies are conceptualized based on the current diagnostic criteria, and controversies and challenges are discussed. Additionally, we analyze the risk factors for cognitive functioning in aging, both modifiable and nonmodifiable ones. A review of the main nonpharmacological intervention techniques used from the gerontology approach is made. It includes the cognitive training in the case of age-related decline or techniques of stimulation and cognitive rehabilitation in the case of mild cognitive impairment or dementia. Finally, we conclude with an analysis of the current state of this topic in the field of gerontology and its relevance in professional practice

Journal of Telecommunications and Information Technology, 2013, nr 2

kwartalni

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge