Developing algorithms inspired by human-like reasoning to solve complex problems

Soft Computing elements such as Fuzzy Logic, Neural Networks, Fuzzy Cognitive Maps and Evolutionary algorithms are employed to deal with complex real-life problems that involve imprecision, uncertainty, partial truth, and approximation. The director of ACTA, Prof Elpiniki I. Papageorgiou has more than 20 years of experience in Soft Computing methods as she is the most acknowledged scientist in the field of Fuzzy Cognitive Maps based on the Microsoft Academic Search, and her name is included for the fourth consecutive year (2020-2023) in the World’s Top 2% of Scientists List at Stanford University ranking, in the field of Artificial Intelligence. Soft computing is a term given to a group of computational techniques that are based on AI and natural selection.

ACTA Research team is mainly focused on FCMs and their applications in:

• Knowledge representation, decision-making
• Time series prediction
• Energy demand management and forecasting
• Image classification

Soft computing methods can deal with imprecision, uncertainty, partial truth, and approximation. Soft computing methods can provide flexible, adaptive, human-friendly and cost-effective solutions for complex real-life problems.

Fuzzy Cognitive Maps (FCMs): FCMs are a way of representing and analyzing complex systems using graphs that show the causal relationships between different factors or concepts. FCMs can handle uncertainty, ambiguity, and approximation, unlike traditional logic or mathematical models. FCMs can be used to model and simulate various phenomena, such as social, political, economic, or environmental systems.