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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Administrative Law In The Automated State, Cary Coglianese
Administrative Law In The Automated State, Cary Coglianese
All Faculty Scholarship
In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated state were …
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
FIU Electronic Theses and Dissertations
The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, …
Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter
Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter
Engineering Management & Systems Engineering Faculty Publications
The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Research Collection School Of Computing and Information Systems
We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …
Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham
Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …
Designing A Portfolio Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir
Designing A Portfolio Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir
Research Collection School Of Computing and Information Systems
Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …
Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani
Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel neural network (NN) -based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback …
Automated Manpower Rostering: Techniques And Experience, C. M. Khoong, Hoong Chuin Lau, L. W. Chew
Automated Manpower Rostering: Techniques And Experience, C. M. Khoong, Hoong Chuin Lau, L. W. Chew
Research Collection School Of Computing and Information Systems
We present ROMAN, a comprehensive, generic manpower rostering toolkit that successfully handles a wide spectrum of work policies found in service organizations. We review the use of various techniques and methodologies in the toolkit that contribute to its robustness and efficiency, and relate experience gained in addressing manpower rostering problems in industry.