Decision-making refers to a process involving ideas and decisions about certain events. It is a complex process in terms of operations, which includes information collection, processing, judgments, and conclusions. Artificial intelligence (AI) is a subject that looks into computer simulations and assesses certain thinking processes and intelligent behaviors of humans, such as learning, reasoning, and planning. AI is mainly based on the principles of computer intelligence, enabling computers to have similar intelligence as human brains. AI and machine learning (ML) can help us make the best choices for decision-making problems. The most commonly used AI and ML tools for decision-making are genetic algorithms, cellular automata, and agent-based models.

The use of AI and ML models has become increasingly widespread especially in data-driven and user-driven applications, where data can be collected and ingested in complex ML models in the cloud and later run on modern powerful handheld devices. At an industrial level, in robotics and embedded systems there are several applications where AI solutions are being tested and new hardware accelerating AI and ML loads even in relatively low cost embedded systems. This new approach to traditional applications is being driven by industry 4.0, making the collection of data in the field easier and more cost effective. AI and ML solutions promise the capability of enabling complex decision making on the edge at the field without the need to send large amounts of data to process offsite or to cloud services.

The aim of this Special Issue is to bring together original research and review articles discussing how artificial intelligence and learning machines in decision-making help conduct further research in computer science, engineering, physics, mathematics, and medicine public policy. This Special Issue hopes to provide a platform for researchers to discuss their new findings in understanding how AI and ML can solve decision-making problems.

Potential topics include but are not limited to the following:

• AI and ML-driven decision-making in system control
• AI and ML models used for for decision-making and control
• AI and ML-driven decision-making in medical expert models
• AI and ML-driven decision-making in fuzzy mathematic models
• AI and ML-driven decision-making in rough mathematic models
• AI and ML-driven decision-making in complex models
• AI and ML-driven decision-making in nonlinear systems control
• AI and ML-driven decision-making in fractional-order systems control
• Cost driven AI and ML design and implementations