Menjana pemodulatan lebar denyut (PWM) penyongsang tiga fasa menggunakan pemproses isyarat digital (DSP)

Baru-baru ini, penyongsang digunakan secara meluas dalam aplikasi industri. Walaubagaimanapun, teknik Pemodulatan Lebar Denyut (PWM) diperlukan untuk mengawal voltan keluaran dan frekuensi penyongsang. Dalam tesis ini, untuk Pemodulatan Lebar Denyut Sinus Unipolar (SPWM) penyongsang tiga fasa adalah dicadang menggunakan Pemproses Isyarat Digital (DSP). Satu model simulasi menggunakan MATLAB Simulink dibangunkan untuk menentukan program Pemodulatan Lebar Denyut Sinus Unipolar (SPWM) Program ini kemudian dibangunkan dalam Pemproses Isyarat Digital (DSP) TMS320f28335. Hasilnya menunjukkan bahawa voltan keluaran penyongsang tiga fasa boleh dikendalikan

Neuroevolution in Games: State of the Art and Open Challenges

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table (Table 1

Evolutionary Machine Learning and Games

Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing games; it is also about generating game content, modeling players, and many other applications. Many of these applications pose interesting problems for EML. We will structure this chapter on EML for games based on whether evolution is used to augment machine learning (ML) or ML is used to augment evolution. For completeness, we also briefly discuss the usage of ML and evolution separately in games.Comment: 27 pages, 5 figures, part of Evolutionary Machine Learning Book (https://link.springer.com/book/10.1007/978-981-99-3814-8

Using Genetic Learning in Weight-Based Game AI

Human beings have been playing games for centuries, and over time, mankind has learned how to excel at these fun competitions. With the ever-growing interest in the field of Machine Learning and Artificial Intelligence (AI), developers have been finding ways to let the game compete against the player much like another human would. While there are many approaches to humanlike learning in machines, this article will focus on using Evolutionary Optimization as a method to develop different levels of pseudo-thinking inan AI used for ato effectively play the Connect Four game

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI

Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game

Le reti neurali artificiali, grazie alle nuove tecniche di Deep Learning, hanno completamente rivoluzionato il panorama tecnologico degli ultimi anni, dimostrandosi efficaci in svariati compiti di Intelligenza Artificiale e ambiti affini. Sarebbe quindi interessante analizzare in che modo e in quale misura le deep network possano sostituire le IA simboliche. Dopo gli impressionanti risultati ottenuti nel gioco del Go, come caso di studio è stato scelto il gioco del Mulino, un gioco da tavolo largamente diffuso e ampiamente studiato. È stato quindi creato il sistema completamente sub-simbolico Neural Nine Men’s Morris, che sfrutta tre reti neurali per scegliere la mossa migliore. Le reti sono state addestrate su un dataset di più di 1.500.000 coppie (stato del gioco, mossa migliore), creato in base alle scelte di una IA simbolica. Il sistema ha dimostrato di aver imparato le regole del gioco proponendo una mossa valida in più del 99% dei casi di test. Inoltre ha raggiunto un’accuratezza del 39% rispetto al dataset e ha sviluppato una propria strategia di gioco diversa da quella della IA addestratrice, dimostrandosi un giocatore peggiore o migliore a seconda dell’avversario. I risultati ottenuti in questo caso di studio mostrano che, in questo contesto, la chiave del successo nella progettazione di sistemi AI allo stato dell’arte sembra essere un buon bilanciamento tra tecniche simboliche e sub-simboliche, dando più rilevanza a queste ultime, con lo scopo di raggiungere la perfetta integrazione di queste tecnologie

PSO-based coevolutionary Game Learning

Games have been investigated as computationally complex problems since the inception of artificial intelligence in the 1950’s. Originally, search-based techniques were applied to create a competent (and sometimes even expert) game player. The search-based techniques, such as game trees, made use of human-defined knowledge to evaluate the current game state and recommend the best move to make next. Recent research has shown that neural networks can be evolved as game state evaluators, thereby removing the human intelligence factor completely. This study builds on the initial research that made use of evolutionary programming to evolve neural networks in the game learning domain. Particle Swarm Optimisation (PSO) is applied inside a coevolutionary training environment to evolve the weights of the neural network. The training technique is applied to both the zero sum and non-zero sum game domains, with specific application to Tic-Tac-Toe, Checkers and the Iterated Prisoners Dilemma (IPD). The influence of the various PSO parameters on playing performance are experimentally examined, and the overall performance of three different neighbourhood information sharing structures compared. A new coevolutionary scoring scheme and particle dispersement operator are defined, inspired by Formula One Grand Prix racing. Finally, the PSO is applied in three novel ways to evolve strategies for the IPD – the first application of its kind in the PSO field. The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity.Dissertation (MSc)--University of Pretoria, 2005.Computer Scienceunrestricte