Chapter 6. Tractable Boolean and Arithmetic Circuits

2021 ◽  
Author(s):  
Adnan Darwiche

Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two decades now. These circuits were initially proposed as “compiled objects,” meant to facilitate logical and probabilistic reasoning, as they permit various types of inference to be performed in linear time and a feed-forward fashion like neural networks. In more recent years, the role of tractable circuits has significantly expanded as they became a computational and semantical backbone for some approaches that aim to integrate knowledge, reasoning and learning. In this chapter, we review the foundations of tractable circuits and some associated milestones, while focusing on their core properties and techniques that make them particularly useful for the broad aims of neuro-symbolic AI.

Author(s):  
V. V. Nefedev

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 


Author(s):  
Siddhartha Satpathi ◽  
Harsh Gupta ◽  
Shiyu Liang ◽  
R Srikant
Keyword(s):  

Author(s):  
J. M. Westall ◽  
M. S. Narasimha

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.


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