Artificial Intelligence for Autonomous Planning and Scheduling of Image Acquisition with the SmartCam App On-Board the OPS-SAT Spacecraft

2022 ◽  
Author(s):  
Georges Labrèche ◽  
David Evans ◽  
Dominik Marszk ◽  
Tom Mladenov ◽  
Vasundhara Shiradhonkar ◽  
...  
1990 ◽  
Vol 5 (3) ◽  
pp. 147-166 ◽  
Author(s):  
Moonis Ali

AbstractAn overview of research in the areas of aerospace applications of artificial intelligence, expert Systems, neural networks and robotics is presented. Challenges associated with aerospace projects require increasingly complex aerospace Systems which in turn demand automation and fault tolerance. We have addressed these issues and provided a survey of the research on intelligent Systems that has been carried out in an attempt to meet these challenges. The application areas we have overviewed include fault monitoring and diagnosis, generation and management of power in space, efficient and effective command and control, operations and maintenance of space stations, planning and scheduling, automation, and cockpit management.


2022 ◽  
Vol 30 (8) ◽  
pp. 0-0

Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.


Author(s):  
Keiki Takadama

This special issue features the selected papers from i-SAIRAS 2010 (The 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space) at Sapporo, Japan on August 29 - September 1, 2010), which explores the technology of Artificial Intelligence (AI), Automation and Robotics, and its application in space. In the AI domain, in particular, i-SAIRAS focuses on the following issues: (1) spacecraft autonomy (e.g., inboard software for mission planning and execution, resource management, fault protection, science data analysis, guidance, navigation and control, smart sensors, testing and validation, architectures); (2) mission operations automation (e.g., decision support tools for mission planning and scheduling, anomaly detection and fault analysis, innovative operations concepts, data visualization, secure commanding and networking); (3) design tools and optimization methods, electronic documentation; and (4) AI methods (e.g., automated planning and scheduling, agents model-based reasoning, machine learning and data mining). In the selection process for JACIII (Journal of Advanced Computational Intelligence and Intelligent Informatics), 13 papers were firstly nominated from 133 oral presentation papers as outstanding AI-related papers by i-SAIRAS International Committee, and 6 papers were finally accepted through the two-stages of pear-reviews. All papers were reviewed by three reviewers. As the brief introduction of these papers, the paper by Mark Johnston and Mark Giuliano presents an architecture called MUSE (Multi-User Scheduling Environment) to integrate multi-objective evolutionary algorithms with existing domain planning and scheduling tools. The second paper by Amdeo Cesta et al. discusses general lessons learned from a series of deployed planning and scheduling systems. The third paper by Alessandro Donati et al. spotlights specific achievements and trends in the area of spacecraft diagnosis and mission planning and scheduling. The fourth paper by Cedric Cocaud and Takashi Kubota proposes the system that provides position and attitude information to a spacecraft during its approach descent and landing phase toward the surface of an asteroid. The firth paper by Tomohiro Harada et al. studies On-Board Computer which evolves computer programs through the bit inversion and analyzes its robustness to the bit inversion. Finally, the last paper by Masayuki Otani et al. explores the distributed control of the multiple robots which may be broken in the assembly of space solar power satellite. The editor hopes that these papers would help for readers to capture the state-of-art of AI technology in space.


2000 ◽  
Vol 15 (1) ◽  
pp. 1-10 ◽  
Author(s):  
CARLA P. GOMES

Both the Artificial Intelligence (AI) and the Operations Research (OR) communities are interested in developing techniques for solving hard combinatorial problems, in particular in the domain of planning and scheduling. AI approaches encompass a rich collection of knowledge representation formalisms for dealing with a wide variety of real-world problems. Some examples are constraint programming representations, logical formalisms, declarative and functional programming languages such as Prolog and Lisp, Bayesian models, rule-based formalism, etc. The downside of such rich representations is that in general they lead to intractable problems, and we therefore often cannot use such formalisms for handling realistic size problems. OR, on the other hand, has focused on more tractable representations, such as linear programming formulations. OR-based techniques have demonstrated the ability to identify optimal and locally optimal solutions for well-defined problem spaces. In general, however, OR solutions are restricted to rigid models with limited expressive power. AI techniques, on the other hand, provide richer and more flexible representations of real-world problems, supporting efficient constraint-based reasoning mechanisms as well as mixed initiative frameworks, which allow the human expertise to be in the loop. The challenge lies in providing representations that are expressive enough to describe real-world problems and at the same time guaranteeing good and fast solutions.


2001 ◽  
Vol 16 (1) ◽  
pp. 1-4 ◽  
Author(s):  
CARLA P. GOMES

This is the second of two special issues focusing on the integration of artificial intelligence (AI) and operations research (OR) techniques for solving hard computational problems, with an emphasis on planning and scheduling. Both the AI and the OR community have developed sophisticated techniques to tackle such challenging problems. OR has relied heavily on mathematical programming formulations such as integer and linear programming, while AI has developed constraint-based search techniques and inference methods. Recently, we have seen a convergence of ideas, drawing on the individual strengths of these paradigms.


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