• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.113-124
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• 2009

How can action learning program promote organizational learning performance and especially project based team performance in Web-based small and medium-sized enterprises (SMEs)? This article discusses the association between project based team in action learning program and the performance of Web-based SME to be learning organization. In the case of ALPACO, action learning program that promote employee communication behavior, knowledge sharing, and organizational learning are found to be positively associated with the project based team performance and organizational learning, The results indicate that action learning program in SMEs indeed associated with greater knowledge sharing, learning communication skills and changing organizational culture. Learning organization can be, in turn, positively developed by project based team through action learning program for creating competitive advantage, Also, this study offers further support for the practical perspective on learning organization performance. The evidence from this case study suggests that the project team in action learning program playa significant role in team performance and the development of learning organization of the firm. Therefore, in the future, Web-based SMEs should consider making investments in action learning program that encourage project team's effective management in decision making, knowledge sharing, and organizational learning.

• Educational Technology International
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• v.9 no.1
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• pp.1-23
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• 2008

Despite considerable commitment to the application of action learning as an organization development intervention, no identified systematic investigation of action learning practices has been reported. Based on a systematic literature review, the purpose of this paper is to identify whether researchers strike a balance between action and learning in their studies of action learning. Research findings in this study included: (1) only 32 empirical studies were found from the electronic database search; (2) based on the hypothesized continuum of Revans' original proposition of balancing action and learning, the author categorized 32 studies into three groups: action-oriented, learning-oriented, and balanced action learning; (3) there were only nine studies on balanced action learning among 32 empirical studies, whose insights included an effective use of project teams, applications of action learning for organization development, and key success factors such as time, reflection, and management support; (4) case study was among the most frequently used research method and only six quality studies met key methodological traits; and (5) therefore, more rigorous empirical research employing quantitative methods as well as case studies is needed to determine whether researchers strike a balance between action and learning in studies on action learning.

• Knowledge Management Research
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• v.4 no.2
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• pp.55-77
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• 2003

In this study, some issues that are related to embody the original conception and ideal of action learning are explored in terms of misunderstanding and misuse of action learning in Korean corporate context. The conception of action learning is deconstructed through the lens of 'action' and 'learning' concept, followed by conceptual analysis to the nature of 'action' and 'learning'. Based upon this conceptual deconstruction of 'action' and 'learning', this study is conducted to categorize the concept of 'action' and 'learning' into several representative attributes. Categorization of 'action' and 'learning' leads to draw some adjectives, for examples, reflective, dynamic, complex, nonlinear, that are critical for characterizing action learning. That is, the nature and ideal of action learning are critically reviewed with the reconceptualization of 'action' and learning, which are deconstructed. The Discussion of final thoughts is on what kinds of knowledge perspectives action learning holds in comparison with those of knowledge management and on how to facilitate knowledge construction and sharing with action learning.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.174-174
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• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.43-57
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• 2005

Action learning motivates students to solve open-ended problems by 'developing skills through doing'. This paper reviews the concept of action learning and discusses the adoption of action learning approach to teach operations management at universities. It presents the design and delivery of an action-learning course at City University of Hong Kong. The course incorporates classroom lectures, tutorials and an action-learning workshop. The experience gained proves that action learning facilitates student participation and teamwork and provides a venue of accelerating learning where enables students to handle dynamic problem situations more effectively. The paper concludes that adopting action-learning approach can help lecturers to enhance quality teaching in operations management courses, and provide an alternate means of effective paradigm other than traditional classroom teaching and/or computer-based training at universities.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.291-296
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• 2008

This paper presents the dodecagon-based Q-leaning and SVM algorithm for object search with multiple robots. We organized an experimental environment with several mobile robots, obstacles, and an object. Then we sent the robots to a hallway, where some obstacles were tying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and SVM to enhance the fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.406-411
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• 2005

In this paper, we present the area-based Q-learning to search a target object using multiple robot. To search the target in Markovian space, the robots should recognize their surrounding at where they are located and generate some rules to act upon by themselves. Under area-based Q-learning, a robot, first of all, obtains 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to search for a target object while navigating in a unknown hallway where some obstacles were placed. In the end of this paper, we presents the results of three algorithms - a random search, area-based action making (ABAM), and hexagonal area-based Q-teaming.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• Journal of muscle and joint health
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• v.24 no.3
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• pp.179-186
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• 2017

Purpose: The purpose of this study was conducted to evaluate the effects of an action learning-based nursing ethics education on the self-assertiveness and ethical values in nursing students. Methods: The study was a non-equivalent control group pretest-posttest design. This study was carried out from October 19 to December 11, 2015. Participants were fifty-six undergraduate nursing students who assigned to either an action learning-based nursing ethics education or traditional lecture. Outcomes were measured assessed self-assertiveness and ethical values using questionnaires. Results: There was a significant improvement in the self-assertiveness in the experimental group who received an action learning-based nursing ethics education than the control group who undertook the traditional lecture (p=.017). However, ethical values were not statistically signigicant between two groups (p=.347). Conclusion: This study demonstrated that an action learning-based nursing ethics education for undergraduate students is very effective in promoting self-assertiveness compared to the traditional lecture.