The Evolution of Computing: AlphaGo

Chen, Jim X.

doi:10.1109/mcse.2016.74

Cited by 147 publications

(63 citation statements)

References 1 publication

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“…Many different AI techniques have been applied to tasks typically associated with human creativity, including the production of visual and moving image art [17], literature and music [18], but also more broadly in areas such as games [19] and game shows [20].…”

Section: Artificial Creativitymentioning

confidence: 99%

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

McCormack¹,

Hutchings²,

Gifford³

et al. 2020

Org. Sound

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Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.

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Section: Artificial Creativitymentioning

confidence: 99%

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

McCormack¹,

Hutchings²,

Gifford³

et al. 2020

Org. Sound

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“…By interacting with the environment, the agent learns to select actions at any state to maximize its total reward. In case of deep learning, for example, AlphaGo (Chen, 2016), the agent learns from the experience of human experts and apply the learned skills to solving the problems in the same domain of the experts. Furthermore, computer agents can learn to play games, for example, Go, by themselves through RL without any input from humans.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based collision avoidance: impact of reward function and knowledge transfer

Liu

Jin

2020

AIEDAM

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AbstractCollision avoidance for robots and vehicles in unpredictable environments is a challenging task. Various control strategies have been developed for the agent (i.e., robots or vehicles) to sense the environment, assess the situation, and select the optimal actions to avoid collision and accomplish its mission. In our research on autonomous ships, we take a machine learning approach to collision avoidance. The lack of available ship steering data of human ship masters has made it necessary to acquire collision avoidance knowledge through reinforcement learning (RL). Given that the learned neural network tends to be a black box, it is desirable that a method is available which can be used to design an agent's behavior so that the desired knowledge can be captured. Furthermore, RL with complex tasks can be either time consuming or unfeasible. A multi-stage learning method is needed in which agents can learn from simple tasks and then transfer their learned knowledge to closely related but more complex tasks. In this paper, we explore the ways of designing agent behaviors through tuning reward functions and devise a transfer RL method for multi-stage knowledge acquisition. The computer simulation-based agent training results have shown that it is important to understand the roles of each component in a reward function and the various design parameters in transfer RL. The settings of these parameters are all dependent on the complexity of the tasks and the similarities between them.

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“…Em muitas ocasiões, há a impossibilidade de tomar decisões ótimas e ainda tem-se o ônus de que as más estratégias são punidas com a derrota [Yannakakis e Togelius 2018, Russell e Norvig 2016. Apesar destas dificuldades, desenvolvimentos de soluções de IA aplicada ao jogos de tabuleiro alcançaram notoriedade mundial, a exemplo de Xadrez [Hsu 2002] e Go [Chen 2016].…”

Section: Introductionunclassified

Análise Comparativa de Funções de Utilidade para o Algoritmo Minimax com Poda Alfa-Beta Aplicadas ao Jogo da Onça

Nascimento¹,

Guedes²

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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O Jogo da Onça é um legado da tradição cultural indígena brasileira. É jogado em um tabuleiro assimétrico com 31 posições no qual uma onça duela contra catorze cachorros e onde os adversários possuem diferentes objetivos. Com o intuito de apresentar resultados preliminares da utilização de técnicas de Inteligência Artificial neste domínio, a árvore de complexidade do jogo foi estimada a partir de uma abordagem força bruta com poda e cuja complexidade resultante mostra-se superior à do Jogo de Damas. Também foram propostas e avaliadas funções de utilidade para o algoritmo Minimax com Otimização de Poda Alfa-Beta. Os resultados das simulações enfatizam desafios no projeto das funções de utilidade para o jogador associado aos cachorros.

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The Evolution of Computing: AlphaGo

Cited by 147 publications

References 1 publication

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Reinforcement learning-based collision avoidance: impact of reward function and knowledge transfer

Análise Comparativa de Funções de Utilidade para o Algoritmo Minimax com Poda Alfa-Beta Aplicadas ao Jogo da Onça

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