What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%.

In the age of knowledge-based economies, open innovation has increasing importance. This study aimed to explore the architectural design approaches that can revitalize innovation activities in the era of knowledge-based economies. This paper investigated global case research campuses, manufacturing systems, and innovation districts where architectural design supports innovation activities. This study developed a research framework of architectural design for innovation and applied it in the selected case studies to generate insights. First, the research campuses selected as case studies included Panopticon, DGIST Education and Research Campuses, and Apple Park. Second, the open innovation of manufacturing system architecture was analyzed through the case studies of the Ford Motor Company, Toyota Motor Corporation, and Rolls-Royce Motor Cars. Third, this paper studied the clustered open innovation architectures of Macquarie Park, One North, and Strijp-S Innovation Districts. The findings revealed how tacit knowledge motivates open innovation through the design of manufacturing systems, research campuses, and innovation districts through real examples and mathematical or concept model building.

The purpose of this study is to address the following research question: What is the relationship between open innovation and firm performance? The study built up a research framework with three factors—i.e., open innovation strategy, time scope, and industry condition—to find out the concrete open innovation effects on firm performance. This study adopted four different research methods. Firstly, we applied the aforementioned factors to a game of life simulation in order to identify the concrete differences of open innovation effects on firm performance. Secondly, the study examined the real dynamics of open innovation effects on firm performance in the aircraft industry—one of the oldest modern industries—through a quantitative patent analysis. It then looked into the effects of major factors that impact open innovation effects. Thirdly, this study developed a mathematical model and tried to open the black box of open innovation effects on firm performance. Lastly, the study logically compiled research on open innovation effects on firm performance through the presentation of a causal loop model and derived the possible implications.