METHODOLOGY AND TOOLS IN KNOWLEDGE-BASED SYSTEMS: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE 1415, SUBSERIES OF LECTURE NOTES IN COMPUTER SCIENCE, edited by Jose Mira, Angel Pasquel del Pobil and Moonis Ali,. Vol. 1, (Springer, Berlin, 1998) xxiv+887 pp.

Robotica ◽  
2000 ◽  
Vol 18 (1) ◽  
pp. 89-92
Author(s):  
C.J. Harwood
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Lecture Notes

scholarly journals Review of Artificial Intelligence

2020 ◽  
pp. 143-171
Author(s):  
K. P. V. Sai Aakarsh ◽  
Adwin Manhar
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Medical Diagnosis ◽  
Computer Application ◽  
Knowledge Based Systems ◽  
Human Race ◽  
Knowledge Based ◽  
Ordinary Person ◽  
Digital Computers

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.

RIGOROUS ENGINEERING PRACTICE AND FORMAL REASONING OF DEEP DOMAIN KNOWLEDGE—THE BASIS OF DEPENDABLE KNOWLEDGE BASED SYSTEMS FOR PROCESS PLANT CONTROL

1993 ◽  
Vol 03 (01) ◽  
pp. 53-98 ◽  
Author(s):  
SANDRO BOLOGNA ◽  
TERJE SIVERTSEN ◽  
HEIKKI VÄLISUO
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Engineering Practice ◽  
Knowledge Based Systems ◽  
Control Engineering ◽  
Engineering Software ◽  
Knowledge Based ◽  
Process Plant ◽  
Verification Process ◽  
The Many

Knowledge based systems are often used to replace humans in solving problems for which only heuristic knowledge on the solution is available. However, there are also important application areas where nonheuristic knowledge is available e.g. in technical documents but where efficient use of the knowledge is impossible without the techniques provided by artificial intelligence. High dependability of these kinds of applications can be achieved if domain knowledge can be represented in a language providing both adequate representational constructs and the required level of formality. In addition, the language should be supported by powerful tools assisting in the verification process. Knowledge Based Systems, despite the different technology employed, are still nothing more than a computer program. Unfortunately, quite a few people building knowledge based systems seem to ignore the many good programming practices that have evolved over the years for producing traditional computer programs. What we need is a framework for the modelling of the KBSs development. In our work, it is claimed that these requirements can be met by utilizing and combining ideas from control engineering, software engineering and artificial intelligence.

Trends in temporal representation and reasoning

1996 ◽  
Vol 11 (3) ◽  
pp. 281-288 ◽  
Author(s):  
Luca Chittaro ◽  
Angelo Montanari
Keyword(s):  
Computer Science ◽  
Knowledge Based Systems ◽  
Logical Foundations ◽  
The Third ◽  
Knowledge Based ◽  
Temporal Representation ◽  
Key West ◽  
Research Areas ◽  
Meaningful Connections ◽  
Or Applications

Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.

scholarly journals Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Grazia Lo Sciuto
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Solar Cells ◽  
Computer Science ◽  
Organic Solar Cells ◽  
Full Potential ◽  
Production Chain ◽  
Lecture Notes ◽  
Occurrence Matrix ◽  
Conversion Efficiencies

The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradation and stability of the technology. In order to extract their full potential, OSCs have to be optimized. On the other hand the production chain of the organic solar cells (OSC) can take advantage of the use of artificial intelligence (AI). In fact the integration into the production workflow makes solar cells more competitive and efficient. This paper presents some applications of the AI for optimization of OSCs production processes Full Text: PDF ReferencesLo Sciuto, G., Capizzi, G., Coco, S., Shikler, R., "Geometric shape optimization of organic solar cells for efficiency enhancement by neural networks." (2017) Lecture Notes in Mechanical Engineering, pp. 789-796. CrossRef Barnea, S.N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., Polap, D., "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network." (2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, pp. 378-389. CrossRef Ye, L.; Hu, H.; Ghasemi, M.; Wang, T.; Collins, B.A.; Kim, J.H.; Jiang, K.; Carpenter, J.H.; Li, H.; Li, Z.; et al. "Quantitative relations between interaction parameter, miscibility and function in organic solar cells." Nat. Mater. 2018, 17, 253-260. CrossRef Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610-621 (1973) CrossRef Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E., Wozniak, M.: Automatic classification of fruit defects based on co-occurrence matrix and neural networks. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 861-867, September 2015. CrossRef

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