Knowledge Representation Formalisms for AI Applications

Dear Colleagues,

In the complex challenge of designing intelligent systems in the Big Data era, an adequate representation of knowledge, sometimes considering uncertainty and incompleteness, and an easy-to-understand approach to automated reasoning are required. These are notable aspects of formal representation systems, suitable for making decisions through software agents trained in solving real problems of different natures such as explainability and interpretability of results, hybrid KR&R-Machine Learning, query answering, cybersecurity, the semantic web, and multi-agent systems. The growing demand for the explainability of AI systems operating in the aforementioned domains is also confirmed by the increasing demand that humans can clearly understand the decisions provided by these systems.

The overall aim of this Special Issue is to collect state-of-the-art research findings on the latest developments, up-to-date issues, and challenges in the field of knowledge representation formalisms in support of AI domains. Proposed submissions should make significant methodological or application contributions. This Special Issue should be of interest to the AI community.

Dr. Gianvincenzo Alfano
Dr. Alejandro Javier García
Dr. Francesco Parisi
Guest Editors

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