2,727 research outputs found
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that
evaluate pairs of agents, for example labeling them `winner' and `loser'. If
the game is approximately transitive, then self-play generates sequences of
agents of increasing strength. However, nontransitive games, such as
rock-paper-scissors, can exhibit strategic cycles, and there is no longer a
clear objective -- we want agents to increase in strength, but against whom is
unclear. In this paper, we introduce a geometric framework for formulating
agent objectives in zero-sum games, in order to construct adaptive sequences of
objectives that yield open-ended learning. The framework allows us to reason
about population performance in nontransitive games, and enables the
development of a new algorithm (rectified Nash response, PSRO_rN) that uses
game-theoretic niching to construct diverse populations of effective agents,
producing a stronger set of agents than existing algorithms. We apply PSRO_rN
to two highly nontransitive resource allocation games and find that PSRO_rN
consistently outperforms the existing alternatives.Comment: ICML 2019, final versio
Playing Smart - Artificial Intelligence in Computer Games
Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
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
- …