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arXiv 2025年

作者： Ochiai, Koji Tahara-Arai, Yuya Kato, Akari Kaizu, Kazunari Kariyazaki, Hirokazu Umeno, Makoto Takahashi, Koichi Kanda, Genki N. Ozaki, Haruka Laboratory for Biologically Inspired Computing RIKEN Center for Biosystems Dynamics Research 6-7-1 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Bioinformatics Laboratory Institute of Medicine University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Ph.D. Program in Humanics School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Laboratory Automation Suppliers’ Association 2-1-8 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Cell Modeling and Simulation Group Exploratory Research Center on Life and Living Systems National Institutes of Natural Sciences 5-1 Higashiyama Myodaiji Aichi Okazaki444-8787 Japan Robotics Division YASKAWA Electric Corporation 2-1 Kurosaki Shiroishi Yahatanishi-ku Kitakyushu Fukuoka806-0004 Japan BAIKEIDO LLC 3-8-1 Asano Kokurakita-ku Kitakyushu Fukuoka802-0001 Japan Graduate School of Media and Governance Keio University 5322 Endo Fujisawa-shi Kanagawa252-0882 Japan Medical Research Laboratory Institute of Integrated Research Institute of Science Tokyo 1-5-45 Yushima Bunkyo-ku Tokyo113-8519 Japan Center for Artificial Intelligence Research University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan

The automation of experiments in life sciences and chemistry has significantly advanced with the development of various instruments and AI technologies. However, achieving full laboratory automation, where experiments conceived by scientists are seamlessly executed in automated laboratories, remains a challenge. We identify the lack of automation in planning and operational tasks—critical human-managed processes collectively termed "care"—as a major barrier. Automating care is the key enabler for full laboratory automation. To address this, we propose the concept of self-maintainability (SeM): the ability of a laboratory system to autonomously adapt to internal and external disturbances, maintaining operational readiness akin to living cells. A SeM-enabled laboratory features autonomous recognition of its state, dynamic resource and information management, and adaptive responses to unexpected conditions. This shifts the planning and execution of experimental workflows, including scheduling and reagent allocation, from humans to the system. We present a conceptual framework for implementing SeM-enabled laboratories, comprising three modules—Requirement manager, Labware manager, and device manager—and a Central manager. SeM not only enables scientists to execute envisioned experiments seamlessly but also provides developers with a design concept that drives the technological innovations needed for full automation. Copyright © 2025, The Authors. All rights reserved.

关键词： Information management

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. del Monte Joshua R. Ehrlich Leon B. Ellwein david Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie d. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat derese devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Experience-Based Identification and Control via Higher-Level Learning and Context discernment

Experience-Based Identification and Control via Higher-Level...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.G. Lendaris NW Computational Intelligence Laboratory (NWCIL) & Systems Science Ph.D. Program Portland State University Portland OR USA

In AI systems so far developed, more knowledge (typically stored as "rules") entails slower processing; in the case of humans, the more knowledge attained (in the form of experience), the speed/efficiency of performing new related tasks is improved. Experience-based (EB) identification and control is explored with the objective of achieving more human-like processes for 'intelligent' computing agents. The notion of experience is being successfully addressed via a novel concept for applying reinforcement learning (RL), called HLLA -higher level learning algorithm. The key idea is to re-purpose the RL method (to a "higher level") such that instead of creating an optimal controller for a given task, an already achieved collection of such solutions for a variety of related contexts is provided (as an experience repository), and HLLA creates a strategy for optimally selecting a solution from the repository. The selection process is triggered by the agent becoming aware that a change in context has occurred, followed by the agent seeking information about what changed -a process here called context discernment - and finally, by selection. Typically, context discernment entails a form of system identification (SId); substantial enhancement of SId is also achieved via the EB methods. Examples are given.

关键词： Context awareness Humans Control systems Learning Optimal control System identification Artificial intelligence Context modeling computational intelligence Laboratories

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On-line system identification using context discernment

On-line system identification using context discernment

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International Joint Conference on Neural Networks, IJCNN 2005

作者： Holmstrom, Lars Santiago, Roberto Lendaris, George G. NW Computational Intelligence Laboratory Portland State University Portland OR 97201 Systems Science Ph.D. Program Department of Systems Science and Electrical and Computer Engineering

ISBN: (纸本)0780390482

Mathematical models are often used in system identification applications. The dynamics of most systems, however, change over time and the sources of these changes cannot always be directly determined or measured. To maintain model accuracy, it is desirable to design system identifiers that can adapt to these dynamical shifts. We use reinforcement learning to train an agent to recognize dynamical changes in a modeled system and to estimate new parameter values for the model. The subsequent actions of this agent are characterized as "moving" the parameterized model on an optimal trajectory in model parameter space. It is found that this method is capable of quickly and accurately discerning the correct parameter values. © 2005 IEEE.

关键词： Identification (control systems)

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Adaptive critic learning with fuzzy utility

Adaptive critic learning with fuzzy utility

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NAFIPS 2004 - Annual Meeting of the North American Fuzzy Information Processing Society: Fuzzy Sets in the Heart of the Canadian Rockies

作者： Matzner, Shari A. Shannon, Thaddeus T. NW Computational Intelligence Lab. Systems Science Ph.D. Program Portland State University P.O. Box 751 Portland OR 97207 United States

Adaptive Critic methods, which approximate dynamic programming, have been used successfully for solving optimal control problems. The Adaptive Critic learning algorithm optimizes a secondary utility function that is the sum of the present and an future primary utility. The primary utility function measures the instantaneous cost incurred for the last action taken and the resulting state. The motivation for using a fuzzy primary utility function comes from the set of control problems for which there is only a qualitative definition of performance - for example, success or failure. Previous work in applying Adaptive Critic methods to this type of problem showed that a crisp definition of success resulted in solutions that met the control objective, but in an undesirable manner. An appropriate fuzzy utility function, on the other hand, is able to generate the optimal solution. Another motivation for incorporating fuzzy techniques into the utility function is to overcome measurement noise. Measurement noise has a significant adverse effect on the reliability and speed of Adaptive Critic learning;by incorporating fuzzy sets into the utility function, the effect of the noise can be mitigated.

关键词： Fuzzy sets

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Adaptive critic learning with fuzzy utility

Adaptive critic learning with fuzzy utility

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： S.A. Matzner T.T. Shannon NW Computational Intelligence Laboratory Systems Science Ph.D. Program Portland State University Portland OR USA

Adaptive critic methods, which approximate dynamic programming, have been used successfully for solving optimal control problems. The adaptive critic learning algorithm optimizes a secondary utility function that is the sum of the present and all future primary utility. The primary utility function measures the instantaneous cost incurred for the last action taken and the resulting state. The motivation for using a fuzzy primary utility function comes from the set of control problems for which there is only a qualitative definition of performance - for example, success or failure. Previous work in applying adaptive critic methods to this type of problem showed that a crisp definition of success resulted in solutions that met the control objective, but in an undesirable manner. An appropriate fuzzy utility function, on the other hand, is able to generate the optimal solution. Another motivation for incorporating fuzzy techniques into the utility function is to overcome measurement noise. Measurement noise has a significant adverse effect on the reliability and speed of adaptive critic learning; by incorporating fuzzy sets into the utility function, the effect of the noise can be mitigated.

关键词： dynamic programming Optimal control programmable control Adaptive control Cost function Control systems computational intelligence Fuzzy sets Noise measurement Neural networks

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Learning with binary-valued utility using derivative adaptive critic methods

Learning with binary-valued utility using derivative adaptiv...

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2004 IEEE International Joint Conference on Neural Networks - Proceedings

作者： Matzner, Shari A. Shannon, Thaddeus T. Lendaris, George G. NW Compl. Intelligence Lab Systems Science Ph.D. Program Portland State University P.O. Box 751 Portland OR 97207 United States

ISBN: (纸本)0780383591

Adaptive Critic methods for reinforcement learning are known to provide consistent solutions to optimal control problems, and are also considered plausible models for cognitive learning processes. This paper discusses binary reinforcement in the context of three Adaptive Critic methods: Heuristic dynamic programming (HdP), dual Heuristic programming (dHP), and Globalized dual Heuristic programming (GdHP). Binary reinforcement arises when the qualitative measure of success is simply "pass" or "fail". We implement binary reinforcement with Adaptive Critic methods for the pole-cart benchmark problem. Results demonstrate two qualitatively dissimilar classes of controllers: those that replicate the system stabilization achieved with quadratic utility, and those that merely succeed at not dropping the pole. It is found that the GdHP method is effective for learning an approximately optimal solution, with results comparable to those obtained via dHP that uses a more informative, quadratic utility function.

关键词： Learning systems

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Accelerating Critic Learning in Approximate dynamic programming Via Value Templates and Perceptual Learning

Accelerating Critic Learning in Approximate Dynamic Programm...

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International Joint Conference on Neural Networks 2003

作者： Shannon, Thaddeus T. Santiago, Roberto A. Lendaris, George G. NW Compl. Intelligence Laboratory Systems Science Ph.D. Program Portland State University Portland OR United States

The concept of value templates and perceptual learning are introduced as refinements to the reinforcement learning (RL) paradigm. We demonstrate a method for accelerating dual Heuristic programming (dHP) critic training using value templates and perceptual learning. Both faster and more stable learning are achieved by using the value template and utilizing its inherent constraints to regularize the perceptual learning task. The method is demonstrated by tuning a neurofuzzy control system for a highly nonlinear 2nd order plant proposed by Sanner and Slotine. We take advantage of the TSK model framework throughout to keep the controller, critic, and model components used in dHP highly interpretable.

关键词： Learning systems

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dual heuristic programming for fuzzy control

Dual heuristic programming for fuzzy control

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： G.G. Lendaris T.T. Shannon L.J. Schultz S. Hutsell A. Rogers Northwest Computational Intelligence Laboratory and Systems Science Ph.D. Program Portland State University USA

Overview material for the special session (tuning fuzzy controllers using adaptive critic based approximate dynamic programming) is provided. The dual heuristic programming (dHP) method of approximate dynamic programming is described and used to design a fuzzy control system. dHP and related techniques have been developed in the neurocontrol context but can be equally productive when used with fuzzy controllers or neuro-fuzzy hybrids. This technique is demonstrated by designing a temperature controller for a simple water bath system. In this example, we take advantage of the TSK model framework to initialize the tunable parameters of our plant model with reasonable problem specific values.

关键词： Fuzzy control dynamic programming Neural networks programmable control Adaptive control Fuzzy neural networks Neurofeedback Context modeling Fuzzy systems computational intelligence

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Adaptive critic design for intelligent steering and speed control of a 2-axle vehicle

Adaptive critic design for intelligent steering and speed co...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.G. Lendaris L. Schultz T. Shannon NW Computational Intelligence Laboratory Systems Science Ph.D. Program and ECE Department Portland State University Portland OR USA

Selected adaptive critic (AC) methods are known to be capable of designing (approximately) optimal control policies for nonlinear plants. The present research focuses on an AC method known as dual heuristic programming (dHP). In particular it is seen as useful to explore correspondences between the form of a utility function and the resulting controllers designed by the dHP method. Based on the task of designing a steering controller for a 2-axle, terrestrial, autonomous vehicle, the present paper relates a pair of critics to "divide the labor" of training the controller. Improvements in convergence of the training process is realized in this way. The controllers designed by the dHP method are reasonably robust, and demonstrate good performance on disturbances not even trained on encountering a patch of ice during a steering maneuver, and encountering a wind gust perpendicular to direction of travel.

关键词： programmable control Adaptive control Intelligent vehicles Velocity control Roads computational intelligence Optimal control Ice design methodology Equations

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