Knowledge Representation and Reasoning

Dear Colleagues,

Knowledge representation and reasoning (KRR) is a well-established and highly relevant research field within artificial intelligence that has contributed significantly to advancements in various research areas, including automated planning, natural language understanding, robotics, cybersecurity, and more. KRR techniques are key drivers of innovation in computer science and have led to the development of efficient algorithms for various tasks.

One of the primary algorithmic aspects of KRR is the use of symbolic reasoning techniques, such as logic programming and constraint satisfaction, which have been instrumental in the development of automated reasoning systems for tasks such as theorem proving, diagnosis, and planning. Another algorithmic aspect of KRR is the use of probabilistic reasoning techniques for dealing with uncertainty and incomplete information, which has led to the development of probabilistic programming and Bayesian networks. 

KRR also involves the use of machine learning techniques for knowledge acquisition and induction, such as inductive logic programming and knowledge discovery in databases. These techniques have been effective in discovering patterns and regularities in large datasets and inducing logical theories from examples.

This Special Issue welcomes submissions that focus on various algorithmic aspects of KRR and their applications. We invite researchers to contribute to this issue to showcase how KRR has led to the development of efficient and effective algorithms for a wide range of applications, making it a crucial research field within artificial intelligence.

Dr. Giovanni Amendola
Guest Editor

Manuscript Submission Information

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