Special Issue on Decision Making Under Risk and Uncertainty Using Systems and Control Theory Approach

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Athanasios A. Pantelous ◽  
Ioannis A. Kougioumtzoglou
Keyword(s):  
Decision Making ◽  
Control Theory ◽  
Theory Approach ◽  
Special Issue ◽  
Risk And Uncertainty ◽  
Decision Making Under Risk ◽  
Systems And Control ◽  
And Control

A Systems and Control Theory Approach for Law and Artificial Intelligence: Demystifying the “Black-Box”

J ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 544-556
Author(s):  
Woodrow Barfield
Keyword(s):  
Artificial Intelligence ◽  
Control Theory ◽  
Conceptual Framework ◽  
Theory Approach ◽  
Controlled System ◽  
Systems And Control ◽  
The Law ◽  
And Control ◽  
External Behavior ◽  
The Relationship

In this paper, I propose a conceptual framework for law and artificial intelligence (AI) that is based on ideas derived from systems and control theory. The approach considers the relationship between the input to an AI-controlled system and the system’s output, which may affect events in the real-world. The approach aims to add to the current discussion among legal scholars and legislators on how to regulate AI, which focuses primarily on how the output, or external behavior of a system, leads to actions that may implicate the law. The goal of this paper is to show that not only is the systems output an important consideration for law and AI but so too is the relationship between the systems input to its desired output, as mediated through a feedback loop (and other control variables). In this paper, I argue that ideas derived from systems and control theory can be used to provide a conceptual framework to help understand how the law applies to AI, and particularly, to algorithmically based systems.

Advanced Computational Intelligence in Control Theory and Applications

1999 ◽  
Vol 3 (2) ◽  
pp. 67-67
Author(s):  
Kazuo Tanaka ◽  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Control Theory ◽  
Soft Computing ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Special Issue ◽  
Systems And Control ◽  
And Control

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

scholarly journals A Systems and Control Theory Approach for Law and Artificial Intelligence: Demystifying the “Black-Box”

J ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 564-576
Author(s):  
Woodrow Barfield
Keyword(s):  
Artificial Intelligence ◽  
Control Theory ◽  
Conceptual Framework ◽  
Theory Approach ◽  
Controlled System ◽  
Systems And Control ◽  
The Law ◽  
And Control ◽  
External Behavior ◽  
The Relationship

In this paper, I propose a conceptual framework for law and artificial intelligence (AI) that is based on ideas derived from systems and control theory. The approach considers the relationship between the input to an AI-controlled system and the system’s output, which may affect events in the real-world. The approach aims to add to the current discussion among legal scholars and legislators on how to regulate AI, which focuses primarily on how the output, or external behavior of a system, leads to actions that may implicate the law. The goal of this paper is to show that not only is the systems output an important consideration for law and AI but so too is the relationship between the systems input to its desired output, as mediated through a feedback loop (and other control variables). In this paper, I argue that ideas derived from systems and control theory can be used to provide a conceptual framework to help understand how the law applies to AI, and particularly, to algorithmically based systems.

scholarly journals Application of systems and control theory-based hazard analysis to radiation oncology

2016 ◽  
Vol 43 (3) ◽  
pp. 1514-1530 ◽  
Author(s):  
Todd Pawlicki ◽  
Aubrey Samost ◽  
Derek W. Brown ◽  
Ryan P. Manger ◽  
Gwe-Ya Kim ◽  
...  
Keyword(s):  
Control Theory ◽  
Radiation Oncology ◽  
Hazard Analysis ◽  
Systems And Control ◽  
And Control
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