[ad_1] Introduction: Answer set programming (ASP) is a declarative programming paradigm that has gained popularity in recent years for solving complex computational problems. Unlike traditional imperative programming languages, ASP allows users to specify the desired…
Read More
[ad_1] Introduction Probabilistic logic programming is a powerful framework that combines the expressiveness of logic programming with the ability to reason under uncertainty. This approach has been widely used in various fields such as artificial…
Read More
[ad_1] Introduction Inductive logic programming (ILP) is a subfield of machine learning and artificial intelligence that combines logic programming and traditional machine learning techniques to induce logic programs from data. Rule learning, which involves the…
Read More
[ad_1] Introduction Causal inference is a crucial aspect of research that helps us understand the relationships between variables and determine cause-and-effect relationships. By identifying the causal relationships between variables, researchers can make informed decisions and…
Read More
[ad_1] Introduction Analogical reasoning has been recognized as a powerful cognitive process that enables individuals to transfer knowledge from one domain to another. This mechanism allows us to draw parallels between similar situations and apply…
Read More
[ad_1] Introduction Commonsense reasoning is a fundamental aspect of human cognition that allows individuals to make inferences, draw conclusions, and navigate the complexities of everyday life based on a set of shared beliefs and assumptions.…
Read More
[ad_1] Introduction Knowledge base completion is a fundamental task in the field of knowledge representation and reasoning. It aims to automatically infer missing facts in a knowledge base by leveraging existing knowledge and relationships. The…
Read More
[ad_1] Introduction In the era of big data, one of the major challenges faced by researchers and organizations is the interoperability of knowledge across different domains and systems. Linked data has emerged as a powerful…
Read More
[ad_1] Introduction In recent years, there has been an increasing interest in graph neural networks, which are powerful tools for learning representations of graph-structured data. Graph attention networks, a type of graph neural network, have…
Read More
[ad_1] Introduction: Recurrent relational networks have emerged as a powerful tool for relational reasoning in various domains such as natural language processing, computer vision, and robotics. These networks have the ability to capture complex relationships…
Read More