scholarly journals Towards Adjustable Autonomy for the Real World

2002 ◽  
Vol 17 ◽  
pp. 171-228 ◽  
Author(s):  
P. Scerri ◽  
D. V. Pynadath ◽  
M. Tambe
Keyword(s):  
Decision Making ◽  
Real World ◽  
Control Strategy ◽  
Control Strategies ◽  
Research Problem ◽  
Individual Agent ◽  
Team Members ◽  
Adjustable Autonomy ◽  
Novel Approach ◽  
Individual Decisions

Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal two key shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects on actions) to an agent's team due to such transfers-of-control. To remedy these problems, this article presents a novel approach to adjustable autonomy, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from an agent to a user or vice versa) and (ii) actions to change an agent's pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high-quality individual decisions to be made with minimal disruption to the coordination of the team. We present a mathematical model of transfer-of-control strategies. The model guides and informs the operationalization of the strategies using Markov Decision Processes, which select an optimal strategy, given an uncertain environment and costs to the individuals and teams. The approach has been carefully evaluated, including via its use in a real-world, deployed multi-agent system that assists a research group in its daily activities.

scholarly journals Patient-Centered Robot-Aided Passive Neurorehabilitation Exercise Based on Safety-Motion Decision-Making Mechanism

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Lizheng Pan ◽  
Aiguo Song ◽  
Suolin Duan ◽  
Zhuqing Yu
Keyword(s):  
Decision Making ◽  
Control Strategy ◽  
Position Control ◽  
Impedance Control ◽  
Control Strategies ◽  
Movement Speed ◽  
Stroke Patients ◽  
Patient Centered ◽  
Motion Speed ◽  
Clinical Experiments

Safety is one of the crucial issues for robot-aided neurorehabilitation exercise. When it comes to the passive rehabilitation training for stroke patients, the existing control strategies are usually just based on position control to carry out the training, and the patient is out of the controller. However, to some extent, the patient should be taken as a “cooperator” of the training activity, and the movement speed and range of the training movement should be dynamically regulated according to the internal or external state of the subject, just as what the therapist does in clinical therapy. This research presents a novel motion control strategy for patient-centered robot-aided passive neurorehabilitation exercise from the point of the safety. The safety-motion decision-making mechanism is developed to online observe and assess the physical state of training impaired-limb and motion performances and regulate the training parameters (motion speed and training rage), ensuring the safety of the supplied rehabilitation exercise. Meanwhile, position-based impedance control is employed to realize the trajectory tracking motion with interactive compliance. Functional experiments and clinical experiments are investigated with a healthy adult and four recruited stroke patients, respectively. The two types of experimental results demonstrate that the suggested control strategy not only serves with safety-motion training but also presents rehabilitation efficacy.

Project Time-Cost-Quality Trade-off Problem: A Novel Approach Based on Fuzzy Decision Making

2020 ◽  
Vol 28 (04) ◽  
pp. 545-567
Author(s):  
Esmaeil Keshavarz ◽  
Abbas Shoul
Keyword(s):  
Decision Making ◽  
Completion Time ◽  
Real Data ◽  
Research Problem ◽  
Fuzzy Decision Making ◽  
Time Cost ◽  
Fuzzy Decision ◽  
Trade Off ◽  
Novel Approach

Trade-off problems concentrate on balancing the main parameters of a project as completion time, total cost and quality of activities. In this study, the problem of project time-cost-quality trade-off is formulated and solved from a new standpoint. For this purpose, completion time and crash cost of project are illustrated as fuzzy goals, also the dependency of implementing time of each activity and its execution-quality is described by a fuzzy number. The overall quality of the project execution is defined as the minimum execution-quality of the project activities that should be maximized. Based on some real assumptions, a three-objective programming problem associated with the time-cost-quality trade-off problem is formulated; then with the aim of identifying a fair and appropriate trade-off, the research problem is reformulated as a single objective linear programming by utilizing a fuzzy decision-making methodology. Generating a final preferred solution, rather than a set of Pareto optimal solutions, and having a reasonable interpretation are two most important advantages of the proposed approach. To explain the practical performance of the proposed models and approach, a time-cost-quality trade-off problem for a project with real data is solved and analyzed.

scholarly journals Identifying Motor Control Strategies and Their Role in Low Back Pain: A Cross-Disciplinary Approach Bridging Neurosciences With Movement Biomechanics

2021 ◽  
Vol 2 ◽  
Author(s):  
Stefan Schmid ◽  
Christian Bangerter ◽  
Petra Schweinhardt ◽  
Michael L. Meier
Keyword(s):  
Low Back Pain ◽  
Motor Control ◽  
Back Pain ◽  
Control Strategy ◽  
Control Strategies ◽  
Treatment Options ◽  
Movement Behavior ◽  
Low Back ◽  
Novel Approach ◽  
Optical Motion

Persistent low back pain (LBP) is a major health issue, and its treatment remains challenging due to a lack of pathophysiological understanding. A better understanding of LBP pathophysiology has been recognized as a research priority, however research on contributing mechanisms to LBP is often limited by siloed research within different disciplines. Novel cross-disciplinary approaches are necessary to fill important knowledge gaps in LBP research. This becomes particularly apparent when considering new theories about a potential role of changes in movement behavior (motor control) in the development and persistence of LBP. First evidence points toward the existence of different motor control strategy phenotypes, which are suggested to have pain-provoking effects in some individuals driven by interactions between neuroplastic, psychological and biomechanical factors. Yet, these phenotypes and their role in LBP need further validation, which can be systematically tested using an appropriate cross-disciplinary approach. Therefore, we propose a novel approach, connecting methods from neuroscience and biomechanics research including state-of-the-art optical motion capture, musculoskeletal modeling, functional magnetic resonance imaging and assessments of psychological factors. Ultimately, this cross-disciplinary approach might lead to the identification of different motor control strategy phenotypes with the potential to translate into clinical research for better treatment options.

scholarly journals A novel control strategy for efficient biological phosphorus removal with carbon-limited wastewaters

2014 ◽  
Vol 70 (4) ◽  
pp. 691-697 ◽  
Author(s):  
Javier Guerrero ◽  
Albert Guisasola ◽  
Juan A. Baeza
Keyword(s):  
Organic Matter ◽  
Control Strategy ◽  
Phosphorus Removal ◽  
Control Strategies ◽  
Treatment Plant ◽  
Theoretical Background ◽  
Open Loop ◽  
Biological Phosphorus Removal ◽  
Novel Approach ◽  
Biological Phosphorus

This work shows the development and the in silico evaluation of a novel control strategy aiming at successful biological phosphorus removal in a wastewater treatment plant operating in an A2/O configuration with carbon-limited influent. The principle of this novel approach is that the phosphorus in the effluent can be controlled with the nitrate setpoint in the anoxic reactor as manipulated variable. The theoretical background behind this control strategy is that reducing nitrate entrance to the anoxic reactor would result in more organic matter available for biological phosphorus removal. Thus, phosphorus removal would be enhanced at the expense of increasing nitrate in the effluent (but always below legal limits). The work shows the control development, tuning and performance in comparison to open-loop conditions and to two other conventional control strategies for phosphorus removal based on organic matter and metal addition. It is shown that the novel proposed strategy achieves positive nutrient removal results with similar operational costs to the other control strategies and open-loop operation.

Collaborative Action Research In Academic Administration

2019 ◽  
Vol 8 (3) ◽  
pp. 227-252
Author(s):  
Bradley C. Thompson
Keyword(s):  
Decision Making ◽  
Action Research ◽  
Collaborative Action Research ◽  
Team Members ◽  
Academic Administration ◽  
Leadership Culture ◽  
Collaborative Action ◽  
Working Together ◽  
Upper Level ◽  
Action Process

This research involved a study exploring the changes in an academic institution expressed through decision-making in a shifting leadership culture. Prior to the study, the school was heavily entrenched in authoritarian and centralized decision-making, but as upper-level administrators were exposed to the concept of collaborative action research, they began making decisions through a reflection and action process. Changing assumptions and attitudes were observed and recorded through interviews at the end of the research period. The research team engaged in sixteen weekly cycles of reflection and action based on an agenda they mutually agreed to and through an analysis of post-research interviews, weekly planning meetings, discussions, and reflection and action cycles. Findings revealed experiences centering around the issues of:  The nature of collaboration- it created discomfort, it created a sense of teamwork, it created difficulty.  The change of environment in the process- team members began to respect each other more, and the process became more enjoyable.  The freedom and change in the process- freedom to voice opinions and to actively listen, the use of experience to lead elsewhere in the school.  How issues of power are better understood by working together- the former process was less collaborative, politics will always be part of the process. As a result of this study, members have started using this decision-making methodology in other areas of administration.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence

scholarly journals Evaluation of a home-based 7-day infection control strategy for healthcare workers following high-risk exposure to severe acute respiratory coronavirus virus 2 (SARS-CoV-2): A cohort study

2020 ◽  
pp. 1-4
Author(s):  
Carla Benea ◽  
Laura Rendon ◽  
Jesse Papenburg ◽  
Charles Frenette ◽  
Ahmed Imacoudene ◽  
...  
Keyword(s):  
High Risk ◽  
Infection Control ◽  
Control Strategy ◽  
Healthcare Workers ◽  
Control Strategies ◽  
Risk Exposure ◽  
Nasopharyngeal Swab ◽  
Home Based ◽  
Respiratory Coronavirus ◽  
Second Wave

Abstract Objective: Evidence-based infection control strategies are needed for healthcare workers (HCWs) following high-risk exposure to severe acute respiratory coronavirus virus 2 (SARS-CoV-2). In this study, we evaluated the negative predictive value (NPV) of a home-based 7-day infection control strategy. Methods: HCWs advised by their infection control or occupational health officer to self-isolate due to a high-risk SARS-CoV-2 exposure were enrolled between May and October 2020. The strategy consisted of symptom-triggered nasopharyngeal SARS-CoV-2 RNA testing from day 0 to day 7 after exposure and standardized home-based nasopharyngeal swab and saliva testing on day 7. The NPV of this strategy was calculated for (1) clinical coronavirus disease 2019 (COVID-19) diagnosis from day 8–14 after exposure, and for (2) asymptomatic SARS-CoV-2 detected by standardized nasopharyngeal swab and saliva specimens collected at days 9, 10, and 14 after exposure. Interim results are reported in the context of a second wave threatening this essential workforce. Results: Among 30 HCWs enrolled, the mean age was 31 years (SD, ±9), and 24 (80%) were female. Moreover, 3 were diagnosed with COVID-19 by day 14 after exposure (secondary attack rate, 10.0%), and all cases were detected using the 7-day infection control strategy: the NPV for subsequent clinical COVID-19 or asymptomatic SARS-CoV-2 detection by day 14 was 100.0% (95% CI, 93.1%–100.0%). Conclusions: Among HCWs with high-risk exposure to SARS-CoV-2, a home-based 7-day infection control strategy may have a high NPV for subsequent COVID-19 and asymptomatic SARS-CoV-2 detection. Ongoing data collection and data sharing are needed to improve the precision of the estimated NPV, and here we report interim results to inform infection control strategies in light of a second wave threatening this essential workforce.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

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