Artificial Intuitions of Generative Design: An Approach Based on Reinforcement Learning

AbstractThis paper proposes a Reinforcement Learning (RL) based design approach that augments existing algorithmic generative processes through the emergence of a form of artificial design intuition. The research presented in the paper is embedded within a highly speculative research project, Artificial Agency, exploring the operation of Machine Learning (ML) in generative design and digital fabrication. After describing the inherent limitations of contemporary generative design processes, the paper compares the three fundamental types of machine learning frameworks in terms of their characteristics and potential impact on generative design. A theoretical framework is defined to demonstrate the methodology of integrating RL with existing generative design procedures, which is further explained with a Random Walk based experimental design example. The paper includes detailed RL definitions as well as critical reflections on its impact and the effects of its implementation. The proposed artificial intuition within this generative approach is currently being further developed through a series of ongoing and proposed research trajectories noted in the conclusion. The ambition of this research is to deepen the integration of intention with machine learning in generative design.

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Learning and control

10.1093/oso/9780199674923.003.0026 ◽

2018 ◽

Author(s):

Ivan Herreros

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Brain Function ◽

Control Strategies ◽

Learning Problems ◽

Animal Learning ◽

Feed Forward Control ◽

Machine Learning Applications ◽

And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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Review on Machine Learning Frameworks in Drivers’ Physiological Signal Analysis to Detect Stress

2021 7th International Conference on Control, Instrumentation and Automation (ICCIA) ◽

10.1109/iccia52082.2021.9403605 ◽

2021 ◽

Author(s):

Maryam Memar ◽

Amin Mokaribolhassan ◽

Amir Aminzadeh Ghavifekr

Keyword(s):

Machine Learning ◽

Signal Analysis ◽

Physiological Signal ◽

Learning Frameworks

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Quantum Reinforcement Learning with Quantum Photonics

Photonics ◽

10.3390/photonics8020033 ◽

2021 ◽

Vol 8 (2) ◽

pp. 33

Author(s):

Lucas Lamata

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Quantum Computation ◽

Exchange Information ◽

Quantum Machine Learning ◽

Quantum Technology ◽

Quantum Photonics

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

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Efficient RCS Prediction of the Conducting Target based on Physics-Inspired Machine Learning and Experimental Design

IEEE Transactions on Antennas and Propagation ◽

10.1109/tap.2020.3027594 ◽

2020 ◽

pp. 1-1

Author(s):

Donghai Xiao ◽

Lixin Guo ◽

Wei Liu ◽

Muyu Hou

Keyword(s):

Machine Learning ◽

Experimental Design

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On the Impact of Multi-language Development in Machine Learning Frameworks

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) ◽

10.1109/icsme46990.2020.00058 ◽

2020 ◽

Author(s):

Manel Grichi ◽

Ellis E. Eghan ◽

Bram Adams

Keyword(s):

Machine Learning ◽

Language Development ◽

The Impact ◽

Learning Frameworks

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How to train your robot with deep reinforcement learning: lessons we have learned

The International Journal of Robotics Research ◽

10.1177/0278364920987859 ◽

2021 ◽

pp. 027836492098785

Author(s):

Julian Ibarz ◽

Jie Tan ◽

Chelsea Finn ◽

Mrinal Kalakrishnan ◽

Peter Pastor ◽

...

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Case Studies ◽

Real World ◽

Review Article ◽

The Real ◽

Complex Skills ◽

Real World Learning ◽

Level Sensor ◽

Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

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Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽

10.3390/en14123654 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3654

Author(s):

Nastaran Gholizadeh ◽

Petr Musilek

Keyword(s):

Machine Learning ◽

Power Systems ◽

Learning Algorithm ◽

Single Point ◽

Distributed Learning ◽

Large Data ◽

Multi Agent Systems ◽

Power Quality Monitoring ◽

Multi Agent ◽

Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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Preventive and Curative Personality Profiling Based on EEG, ERP, and Big Five Personality Traits: A Literature Review

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8909 ◽

2020 ◽

Vol 17 (2) ◽

pp. 531-545

Author(s):

Cut Amalia Saffiera ◽

Raini Hassan ◽

Amelia Ritahani Ismail

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Experimental Design ◽

Personality Traits ◽

Big Five ◽

Healthy Lifestyle ◽

Big Five Personality Traits ◽

Event Related Potential ◽

Big Five Personality ◽

Electrophysiological Signals

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning.

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