The Experts below are selected from a list of 7509 Experts worldwide ranked by ideXlab platform
Amir G. Aghdam - One of the best experts on this subject based on the ideXlab platform.
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Stability analysis of Dynamic Decision-making for vehicle heading control
2015 American Control Conference (ACC), 2015Co-Authors: Mohammad Khosravi, Amir G. AghdamAbstract:In this paper, the problem of Dynamic Decision making for vehicle heading control to intercept moving targets is investigated. It is assumed that the targets arrive in the mission space sequentially. More precisely, there exist infinite number of targets that arrive the mission space one by one. The arrival times and positions of the targets are modeled using stochastic models. Furthermore, targets are assumed to move with unknown Dynamics and unknown trajectories. Due to the probabilistic nature of the problem, it is desired to use a model predictive approach to control the heading of the vehicle. A reward allocation strategy is adopted for Dynamic Decision making and control design in order to move the vehicle toward the targets. Finite-time convergence analysis is presented for the case where the arrivals of targets occur sufficiently infrequently.
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ACC - Stability analysis of Dynamic Decision-making for vehicle heading control
2015 American Control Conference (ACC), 2015Co-Authors: Mohammad Khosravi, Amir G. AghdamAbstract:In this paper, the problem of Dynamic Decision making for vehicle heading control to intercept moving targets is investigated. It is assumed that the targets arrive in the mission space sequentially. More precisely, there exist infinite number of targets that arrive the mission space one by one. The arrival times and positions of the targets are modeled using stochastic models. Furthermore, targets are assumed to move with unknown Dynamics and unknown trajectories. Due to the probabilistic nature of the problem, it is desired to use a model predictive approach to control the heading of the vehicle. A reward allocation strategy is adopted for Dynamic Decision making and control design in order to move the vehicle toward the targets. Finite-time convergence analysis is presented for the case where the arrivals of targets occur sufficiently infrequently.
Cleotilde Gonzalez - One of the best experts on this subject based on the ideXlab platform.
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Effects of Automatic Detection on Dynamic Decision Making
Journal of Cognitive Engineering and Decision Making, 2020Co-Authors: Cleotilde Gonzalez, Rickey P. ThomasAbstract:The usefulness of the dual-process theory of automaticity to Dynamic Decision-making tasks is unclear. Dynamic Decision making is characterized by multiple, diverse, and interrelated Decisions that are often constrained by time limitations and workload. We investigated the relevance of this theory in a compound task consisting of multiple, Dynamic components. In the first experiment, we reproduced the original findings that shaped the theory in a Dynamic visual search task. In the second experiment, we added a Decision-making component. The results replicated the original findings and extended them to Decision-making components. Working under consistent-mapping conditions in the visual search component led to more accurate Decision making despite variability in the Decision-making conditions. Likewise, working under varied-mapping conditions in the visual search component led to poorer Decision making, particularly under high workload. The implications of these results to real-world Dynamic tasks are discussed.
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Dynamic Decision Making: Learning Processes and New Research Directions:
Human Factors, 2017Co-Authors: Cleotilde Gonzalez, Pegah Fakhari, Jerome R. BusemeyerAbstract:Objective:The aim of this manuscript is to provide a review of contemporary research and applications on Dynamic Decision making (DDM).Background:Since early DDM studies, there has been little syst...
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Decision support for real time Dynamic Decision making tasks
Organizational Behavior and Human Decision Processes, 2005Co-Authors: Cleotilde GonzalezAbstract:Abstract By definition, Dynamic Decision making dictates that multiple and interrelated Decisions be made in a continuously changing environment. Such Decision making is difficult and often taxes individuals’ cognitive resources. Here I investigated ways in which to support Decision making in these environments. I evaluated three forms of Decision support: outcome feedback, cognitive feedback, and feedforward that incorporated (to varying degrees) common features of learning theories associated with Dynamic tasks. Participants in a laboratory experiment performed a real-time, Dynamic Decision-making task while receiving one of the different types of Decision support. During the first 2 days, individuals received one type of Decision support, but on the third day they performed the task without this support. Participants who received feedforward improved their performance considerably and continued to exhibit improved performance even after discontinuation of the Decision support on the third day. Neither outcome feedback nor cognitive feedback resulted in improved performance. More research is necessary to conclusively identify the best forms of Dynamic Decision-making support and their durability when transferred to new tasks.
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The relationships between cognitive ability and Dynamic Decision making
Intelligence, 2005Co-Authors: Cleotilde Gonzalez, Rickey P. Thomas, Polina M. VanyukovAbstract:This study investigated the relationships between cognitive ability (as assessed by the Raven Progressive Matrices Test [RPM] and the Visual-Span Test [VSPAN]) and individuals' performance in three Dynamic Decision making (DDM) tasks (i.e., regular Water Purification Plant [WPP], Team WPP, and Firechief). Participants interacted repeatedly with one of the three microworlds. Our results indicate a positive association between VSPAN and RPM scores and between each of those measures and performance in the three Dynamic tasks. Practice had no effect on the correlation between RPM score and performance in any of the microworlds, but it led to an increased correlation between VSPAN score and performance in Team WPP. The pattern of associations between performance in microworlds and assessments of cognitive ability was consistent with the task requirements of the microworlds. These findings provide insight into the cognitive demands of Dynamic Decision making and the Dynamics of the relationships between cognitive ability and performance with task practice.
Yanping Xiang - One of the best experts on this subject based on the ideXlab platform.
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A knowledge-based modeling system for time-critical Dynamic Decision-making
Lecture Notes in Computer Science, 2020Co-Authors: Yanping XiangAbstract:Knowledge-based model construction approach has been applied to many problems. Previous research doesn't provide an approach to construct models to deal with time-critical Dynamic Decision problems. This paper presents a Knowledge-based Time-critical Dynamic Decision Modeling system (KTDDM) for time-critical Dynamic Decision-making. The system architecture and functional modules are described. A new knowledge representation framework is provided to support the whole model construction process. This paper applies the KTDDM system prototype to construct a Decision model for the time-critical Dynamic medical problem on cardiac arrest to demonstrate the effectiveness of this approach.
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Time-Critical Dynamic Decision Making
arXiv: Artificial Intelligence, 2013Co-Authors: Yanping XiangAbstract:Recent interests in Dynamic Decision modeling have led to the development of several representation and inference methods. These methods however, have limited application under time critical conditions where a trade-off between model quality and computational tractability is essential. This paper presents an approach to time-critical Dynamic Decision modeling. A knowledge representation and modeling method called the time-critical Dynamic influence diagram is proposed. The formalism has two forms. The condensed form is used for modeling and model abstraction, while the deployed form which can be converted from the condensed form is used for inference purposes. The proposed approach has the ability to represent space-temporal abstraction within the model. A knowledge-based meta-reasoning approach is proposed for the purpose of selecting the best abstracted model that provide the optimal trade-off between model quality and model tractability. An outline of the knowledge-based model construction algorithm is also provided.
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PRICAI - An influence diagram approach for multiagent time-critical Dynamic Decision modeling
PRICAI 2010: Trends in Artificial Intelligence, 2010Co-Authors: Yifeng Zeng, Yanping XiangAbstract:Recent interests in multiagent Dynamic Decision modeling in partially observable multiagent environments have led to the development of several representation and inference methods. However, these methods have limited application under time-critical conditions where a trade-off between model quality and computational tractability is essential. We present a formal representation for modeling time-critical multiagent Dynamic Decision problems through interactive Dynamic influence diagrams. The proposed model, called interactive time-critical Dynamic influence diagrams, has the ability to represent space-temporal abstraction in multiagent Dynamic Decision models. More importantly, we take the notion of object-orientation design which facilitates the self-expansion and self-compression in the model implementation.
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PRICAI - A knowledge-based modeling system for time-critical Dynamic Decision-making
Lecture Notes in Computer Science, 2006Co-Authors: Yanping XiangAbstract:Knowledge-based model construction approach has been applied to many problems. Previous research doesn't provide an approach to construct models to deal with time-critical Dynamic Decision problems. This paper presents a Knowledge-based Time-critical Dynamic Decision Modeling system (KTDDM) for time-critical Dynamic Decision-making. The system architecture and functional modules are described. A new knowledge representation framework is provided to support the whole model construction process. This paper applies the KTDDM system prototype to construct a Decision model for the time-critical Dynamic medical problem on cardiac arrest to demonstrate the effectiveness of this approach.
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UAI - Time-critical Dynamic Decision making
1999Co-Authors: Yanping XiangAbstract:Recent interests in Dynamic Decision modeling have led to the development of several representation and inference methods. These methods however, have limited application under time critical conditions where a trade-off between model quality and computational tractability is essential. This paper presents an approach to time-critical Dynamic Decision modeling. A knowledge representation and modeling method called the time-critical Dynamic influence diagram is proposed. The formalism has two forms. The condensed form is used for modeling and model abstraction, while the deployed form which can be converted from the condensed form is used for inference purposes. The proposed approach has the ability to represent space-temporal abstraction within the model. A knowledge-based meta-reasoning approach is proposed for the purpose of selecting the best abstracted model that provide the optimal trade-off between model quality and model tractability. An outline of the knowledge-based model construction algorithm is also provided.
Mohammad Khosravi - One of the best experts on this subject based on the ideXlab platform.
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Stability analysis of Dynamic Decision-making for vehicle heading control
2015 American Control Conference (ACC), 2015Co-Authors: Mohammad Khosravi, Amir G. AghdamAbstract:In this paper, the problem of Dynamic Decision making for vehicle heading control to intercept moving targets is investigated. It is assumed that the targets arrive in the mission space sequentially. More precisely, there exist infinite number of targets that arrive the mission space one by one. The arrival times and positions of the targets are modeled using stochastic models. Furthermore, targets are assumed to move with unknown Dynamics and unknown trajectories. Due to the probabilistic nature of the problem, it is desired to use a model predictive approach to control the heading of the vehicle. A reward allocation strategy is adopted for Dynamic Decision making and control design in order to move the vehicle toward the targets. Finite-time convergence analysis is presented for the case where the arrivals of targets occur sufficiently infrequently.
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ACC - Stability analysis of Dynamic Decision-making for vehicle heading control
2015 American Control Conference (ACC), 2015Co-Authors: Mohammad Khosravi, Amir G. AghdamAbstract:In this paper, the problem of Dynamic Decision making for vehicle heading control to intercept moving targets is investigated. It is assumed that the targets arrive in the mission space sequentially. More precisely, there exist infinite number of targets that arrive the mission space one by one. The arrival times and positions of the targets are modeled using stochastic models. Furthermore, targets are assumed to move with unknown Dynamics and unknown trajectories. Due to the probabilistic nature of the problem, it is desired to use a model predictive approach to control the heading of the vehicle. A reward allocation strategy is adopted for Dynamic Decision making and control design in order to move the vehicle toward the targets. Finite-time convergence analysis is presented for the case where the arrivals of targets occur sufficiently infrequently.
Tze-yun Leong - One of the best experts on this subject based on the ideXlab platform.
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MedInfo - Constructing influence views from data to support Dynamic Decision making in medicine.
Studies in health technology and informatics, 2020Co-Authors: Xinzhi Qi, Tze-yun LeongAbstract:A Dynamic Decision model can facilitate the complicated Decision-making process in medicine, in which both time and uncertainty are explicitly considered. In this paper, we address the problem of automatic construction of a Dynamic Decision model from a large medical database. Within the DynaMoL (a Dynamic Decision modeling language) framework, a model can be represented in influence view. Thus, our proposed approach first learns the structures of the influence view based on the minimal description length (MDL) principle, and then obtains the conditional probabilities of the model by Bayesian method. The experiment results demonstrate that our system can efficiently construct the influence views from data with high fidelity.
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Dynamic Decision analysis in medicine: a data-driven approach
International Journal of Medical Informatics, 1998Co-Authors: Tze-yun Leong, A. F. P. K. Leong, Francis Choen SeowAbstract:Dynamic Decision analysis concerns Decision problems in which both time and uncertainty are explicitly considered. Two major challenges in Dynamic Decision analysis are on proper formulation of a model for the problem and effective elicitation of the numerous time-dependent conditional probabilities for the model. Based on a new, general Dynamic Decision modeling framework called DynaMoL (Dynamic Decision Modeling Language), we propose a data-driven approach to addressing these issues. Our approach uses available problem data from large medical databases, guides the Decision modeling at a proper level of abstraction and establishes a Bayesian learning method for automatic extraction of the probabilistic parameters. We demonstrate the theoretical implications and practical promises of this new approach to Dynamic Decision analysis in medicine through a comprehensive case study in the optimal follow-up of patients after curative colorectal cancer surgery.
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Multiple perspective Dynamic Decision making
Artificial Intelligence, 1998Co-Authors: Tze-yun LeongAbstract:AbstractDecision making often involves deliberations in different perspectives. Distinct perspectives or views support knowledge acquisition and representation suitable for different types or stages of inference in the same discourse. This work presents a general paradigm for multiple perspective Decision making over time and under uncertainty. Based on a unifying task definition and a common vocabulary for the relevant Decision problems, this new paradigm balances the trade-off between model transparency and solution efficiency in current Decision frameworks.The new paradigm motivates the design of DynaMoL (Dynamic Decision Modeling Language), a general language for modeling and solving Dynamic Decision problems. The DynaMoL framework differentiates inferential and representational support for the modeling task from the solution or computation task. The Dynamic Decision grammar defines an extensible Decision ontology and supports complex problem specification with multiple interfaces. The graphical presentation convention governs parameter visualization in multiple perspectives. The mathematical representation as semi-Markov Decision process facilitates formal model analysis and admits multiple solution methods. A set of general translation techniques is devised to manage the different perspectives and representations of the Decision parameters and constraints. DynaMoL has been evaluated on a prototype implementation, via some comprehensive case studies in medicine. The results demonstrate practical promise of the framework
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Knowledge-based formulation of Dynamic Decision models
Lecture Notes in Computer Science, 1998Co-Authors: Chenggang Wang, Tze-yun LeongAbstract:We present a new methodology to automate Decision making over time and uncertainty. We adopt a knowledge-based model construction approach to support automated and interactive formulation of Dynamic Decision models, i.e., models that explicitly consider the effects of time. Our work integrates and extends different features of the existing frameworks. We incorporate a hybrid knowledge representation scheme that integrates categorical knowledge, probabilistic knowledge, and deterministic knowledge. We provide a set of knowledge-based modification operations for automatic and interactive generation, abstraction, and refinement of the model components. We have built a knowledge base in a real-world domain and shown that it can support automated construction of a reasonable Dynamic Decision model. The results indicate the practical promise of the proposed design.
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PRICAI - Knowledge-Based Formulation of Dynamic Decision Models
PRICAI’98: Topics in Artificial Intelligence, 1998Co-Authors: Chenggang Wang, Tze-yun LeongAbstract:We present a new methodology to automate Decision making over time and uncertainty. We adopt a knowledge-based model construction approach to support automated and interactive formulation of Dynamic Decision models, i.e., models that explicitly consider the effects of time. Our work integrates and extends different features of the existing frameworks. We incorporate a hybrid knowledge representation scheme that integrates categorical knowledge, probabilistic knowledge, and deterministic knowledge. We provide a set of knowledge-based modification operations for automatic and interactive generation, abstraction, and refinement of the model components. We have built a knowledge base in a real-world domain and shown that it can support automated construction of a reasonable Dynamic Decision model. The results indicate the practical promise of the proposed design.
