Igor Sheremet (November 21st 2018). Multiset-Based Knowledge Representation for the Assessment and Optimization of Large-Scale Sociotechnical Systems, Enhanced Expert Systems, Petrică Vizureanu, IntechOpen, DOI: 10.5772/intechopen.81698.

Martin Žáček, Alena Lukasová, Marek Vajgl and Petr Raunigr (August 22nd 2019). Knowledge Patterns within the Conception of Semantic Web [Online First], IntechOpen, DOI: 10.5772/intechopen.88692.

Malta

The purpose of the International Conference on Agents and Artificial Intelligence is to bring together researchers, engineers and practitioners interested in the theory and applications in the areas of Agents and Artificial Intelligence. Two simultaneous related tracks will be held, covering both applications and current research work. One track focuses on Agents, Multi-Agent Systems and Software Platforms, Distributed Problem Solving and Distributed AI in general. The other track focuses mainly on Artificial Intelligence, Knowledge Representation, Planning, Learning, Scheduling, Perception Reactive AI Systems, and Evolutionary Computing and other topics related to Intelligent Systems and Computational Intelligence.

ICAART 2020

The purpose of the International Conference on Agents and Artificial Intelligence is to bring together researchers, engineers and practitioners interested in the theory and applications in the areas of Agents and Artificial Intelligence.

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation. IJCCI is composed of three co-located conferences, each specialized in at least one of the aforementioned main knowledge areas.

IJCCI 2019

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation.

How to exploit knowledge to make better use of data is a timely issue at the crossroad of knowledge representation and reasoning, data management, and the semantic web. Knowledge representation is emblematic of the symbolic approach of Artificial Intelligence based on the development of explicit logic-based models processed by generic reasoning algorithms that are founded in logic. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. Similarly to humans, computer systems can base decisions on reasoning about domain knowledge. And humans can express their data analysis needs using terms of a shared vocabulary in their domain of interest or of expertise. In this talk, I will show how reasoning on data can help to solve in a principled way several problems raised by modern data-centered applications in which data may be ubiquitous, multi-form, multi-source and musti-scale. I will also show how models and algorithms have evolved to face scalability issues and data quality challenges.

Reasoning on Data: Challenges and Application

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Reasoning on Data: Challenges and Application

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