Problem Solving Methods In Artificial Intelligence Ppt
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Problem-Solving Methods
Author: Dieter Fensel
language: en
Publisher: Springer Science & Business Media
Release Date: 2000-09-27
This book provides a theory, a formal language, and a practical methodology for the specification, use, and reuse of problem-solving methods. The framework developed by the author characterizes knowledge-based systems as a particular type of software architecture where the applications are developed by integrating generic task specifications, problem solving methods, and domain models: this approach turns knowledge engineering into a software engineering discipline. All in all, this work, as an applicable theory of knowledge engineering, consolidates research work done during several decades. The present popularity of Internet-based services will provide unprecedented opportunities for deploying and sharing knowledge-based services and anybody wanting to participate in this area can learn from this book what knowledge engineering is about.
Proceedings of the International Conference on Marching Beyond the Libraries (ICMBL): Leadership, Creativity, and Innovation (ICMBL 2024)
Author: Bijayalaxmi Rautaray
language: en
Publisher: Springer Nature
Release Date: 2025-06-16
This is an open access book. The International Conference theme, “Marching Beyond the Libraries: Leadership, Creativity, and Innovation,” encapsulates the dynamic evolution of libraries as crucial hubs of knowledge in the contemporary global landscape. This thought-provoking theme reflects the imperative for libraries to transcend traditional roles, embrace transformative leadership, foster creativity, and drive innovation in order to remain relevant and impactful in an ever-changing world.
Artificial Intelligence for Solar Photovoltaic Systems
This book provides a clear explanation of how to apply artificial intelligence (AI) to solve the challenges in solar photovoltaic technology. It introduces readers to new AI-based approaches and technologies that help manage and operate solar photovoltaic systems effectively. It also motivates readers to find new AI-based solutions for these challenges by providing a comprehensive collection of findings on AI techniques. It covers important topics including solar irradiance variability, solar power forecasting, solar irradiance forecasting, maximum power point tracking, hybrid algorithms, swarm optimization, evolutionary optimization, sensor-based sun- tracking systems, single-axis and dual-axis sun-tracking systems, smart metering, frequency regulation using AI, emerging multilevel inverter topologies, and voltage and reactive power control using AI. This book is useful for senior undergraduate students, graduate students, and academic researchers in areas such as electrical engineering, electronics and communication engineering, computer science, and renewable energy.