It is the subfield of computer science that focuses on creating systems capable of intelligent behavior, including problem solving, learning, adaptation, perception, and language understanding.
The article presents IICONGRAPH, a knowledge graph developed to address gaps in the representation of iconographic and iconological statements in cultural heritage. The graph was created by refining and extending…
Continue readingThe paper presents an innovative automated system for generating architectural schematic designs. This system is designed to streamline complex decision-making processes at the outset of multifamily real estate development projects….
Continue readingThe paper discusses the limitations of the standard evaluation protocol for Knowledge Graph Completion methods, which involves ranking every entity of a Knowledge Graph. The authors argue that this task…
Continue readingThe paper discusses an approach to model epistemic uncertainty in Conjunction Data Messages (CDMs) and classify conjunction events based on the confidence in the collision probability. The proposed approach uses…
Continue readingThis paper discusses the development of an automated system to help people with cognitive disabilities, such as autism, manage transition plans. The system uses a multi-agent planning approach to generate…
Continue readingThis academic paper explores the resolution of the 3-SAT problem using a QAOA-like approach. The authors model the solution ranks of the 3-SAT problem, which, in this specific case, directly…
Continue readingThe Conditional Markov Chain Search (CMCS) is a framework for designing metaheuristics for discrete combinatorial optimization problems. It decides the order of application of algorithmic components like hill climbers and…
Continue readingThe article presents a study motivated by the disparities in COVID-19 vaccine allocation. It formalizes the problem of resource allocation when there are inherent access differences that correlate with advantage…
Continue readingThis research paper explores the fusion of computational experiments and Large Language Models (LLMs) in studying complex systems. Computational experiments are valuable for studying complex systems, but accurately representing real…
Continue readingThe article introduces RadDQN, a deep Q-learning architecture aimed at achieving maximum radiation protection in the nuclear industry. The system operates on a radiation-aware reward function and employs unique exploration…
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