Rainer Knauf | Evolutionary Algorithms | Lifetime achievement Award

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Prof Dr Rainer Knauf |  Evolutionary Algorithms |  Lifetime achievement Award

Fachgebietsleiter für KI at  Technische Universität Ilmenau, Germany

Rainer Knauf is an apl. Prof. Dr.-Ing. habil., currently serving as the Chair of Artificial Intelligence at the Faculty of Computer Science and Automation, Technical University Ilmenau, Germany. He earned his Diploma Engineer (Dipl.-Ing.) in Electrical and Computer Engineering in 1987, followed by a Doctor of Engineering (Dr.-Ing.) in Computer Engineering in 1990, and a Doctor of Engineering habilitatus (Dr.-Ing. habil.) in Computer Science in 2000, all from Technical University Ilmenau. His research focuses on knowledge acquisition, validation, and refinement of intelligent systems, inductive inference, and machine learning.

 

profile

🎓 Education:

  • Dipl.-Ing. in Electrical and Computer Engineering
    Technical University Ilmenau, Germany
    📅 February 5, 1987
  • Dr.-Ing. in Computer Engineering
    Technical University Ilmenau, Germany
    📅 September 25, 1990
    Dissertation: “Applying Logic Programming to Design Knowledge Based Systems for Diagnostic Problems”
  • Dr.-Ing. habil. in Computer Science
    Technical University Ilmenau, Germany
    📅 November 15, 2000
    Habilitation: “Validating Rule Based Systems: A Complete Methodology”

💼 Professional Experience:

  • Full Professor (apl. Prof.)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 March 2010 – Present
  • Associate Professor (Privatdozent)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 April 2004 – February 2010
  • Assistant Professor (Privatdozent)
    Technical University Ilmenau
    📅 December 2000 – March 2004
  • Scientific Assistant
    Technical University Ilmenau
    📅 September 1991 – November 2000
  • Scientific Associate
    Ilmenau Institute of Technology
    📅 March 1987 – August 1991

🏅 Awards & Recognitions

  • Fellowship Awards from the Japan Society for the Promotion of Science 📜 (2008, 2011, 2015)
  • Graduate Faculty Scholar at the University of Central Florida 🎓 (2010)

Research Focus: Evolutionary Algorithms 🧬💡

Research Interests:

  • Optimization and Search Algorithms: Rainer Knauf’s work in evolutionary algorithms involves developing and improving algorithms for optimization and search problems. These algorithms are inspired by the principles of natural selection and genetics.
  • Artificial Intelligence Applications: He applies evolutionary algorithms to various AI challenges, including machine learning, robotics, and automated reasoning.
  • Knowledge Acquisition and Refinement: His research integrates evolutionary algorithms with knowledge-based systems to enhance the processes of knowledge acquisition, validation, and refinement.
  • Data Mining: Knauf explores the use of evolutionary algorithms in data mining, particularly in extracting meaningful patterns and insights from large datasets.
  • Inductive Inference: His work also includes using evolutionary algorithms for inductive inference, aiming to generalize from specific data to broader rules or patterns.

Citation:

Cited by:

  • All: 1082 citations
  • Since 2019: 253 citations

h-index:

  • Overall: 16
  • Since 2019: 7

i10-index:

  • Overall: 33
  • Since 2019: 4

Publication Top Notes:

  • “Didactic design through storyboarding: Standard concepts for standard tools”
    • Authors: KP Jantke, R Knauf
    • Publication: Proceedings of the 4th International Symposium on Information and Communication Technologies
    • Citations: 122 (2005)
    • Summary: This paper explores the use of storyboarding as a method for didactic design, emphasizing standard concepts to standardize tools for educational purposes.
  • “A framework for validation of rule-based systems”
    • Authors: R Knauf, AJ Gonzalez, T Abel
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
    • Citations: 80 (2002)
    • Summary: This paper presents a comprehensive framework for validating rule-based systems, addressing the need for systematic validation processes in artificial intelligence.
  • “Validation of human behavior representation”
    • Authors: SY Harmon, VB Barr, AJ Gonzalez, DC Hoffmann, R Knauf
    • Publication: University Library
    • Citations: 45 (2006)
    • Summary: The authors discuss methodologies for validating models of human behavior representation, crucial for developing reliable AI systems that simulate human actions.
  • “Modeling didactic knowledge by storyboarding”
    • Authors: R Knauf, Y Sakurai, S Tsuruta, KP Jantke
    • Publication: Journal of Educational Computing Research
    • Citations: 39 (2010)
    • Summary: This research focuses on the use of storyboarding to model didactic knowledge, enhancing the design and delivery of educational content through structured visual methods.
  • “Toward reducing human involvement in validation of knowledge-based systems”
    • Authors: R Knauf, S Tsuruta, AJ Gonzalez
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans
    • Citations: 25 (2006)
    • Summary: This paper proposes methods to minimize human intervention in the validation process of knowledge-based systems, aiming for more autonomous and efficient validation techniques.
  • “Tweet credibility analysis evaluation by improving sentiment dictionary”
    • Authors: T Kawabe, Y Namihira, K Suzuki, M Nara, Y Sakurai, S Tsuruta, R Knauf
    • Publication: 2015 IEEE Congress on Evolutionary Computation (CEC)
    • Citations: 24 (2015)
    • Summary: This work evaluates the credibility of tweets by enhancing sentiment dictionaries, leveraging evolutionary computation techniques to improve the accuracy of sentiment analysis.
  • “A simple optimization method based on backtrack and GA for delivery schedule”
    • Authors: Y Sakurai, K Takada, N Tsukamoto, T Onoyama, R Knauf, S Tsuruta
    • Publication: 2011 IEEE Congress of Evolutionary Computation (CEC)
    • Citations: 22 (2011)
    • Summary: The authors present an optimization method combining backtracking and genetic algorithms (GA) to improve delivery scheduling, demonstrating the application of evolutionary algorithms in logistics.
  • “Generation of a minimal set of test cases that is functionally equivalent to an exhaustive set, for use in knowledge-based system validation”
    • Authors: T Abel, R Knauf, AJ Gonzalez
    • Publication: Proceedings of the 9th FLAIRS Conference
    • Citations: 22 (1996)
    • Summary: This paper discusses a method for generating a minimal set of test cases that maintains functional equivalence to an exhaustive set, enhancing the efficiency of knowledge-based system validation.
  • “Modeling academic education processes by dynamic storyboarding”
    • Authors: Y Sakurai, S Dohi, S Tsuruta, R Knauf
    • Publication: Journal of Educational Technology & Society
    • Citations: 21 (2009)
    • Summary: The study models academic education processes through dynamic storyboarding, offering a structured approach to designing and implementing educational curricula.
  • “Validating Rule-Based Systems: A Complete Methodology”
    • Author: R Knauf
    • Publication: Shaker
    • Citations: 21 (2000)
    • Summary: This book provides a comprehensive methodology for the validation of rule-based systems, detailing systematic approaches to ensure the reliability and accuracy of these systems.

 

 

Zhidong CAO | Artificial Intelligence Award | Best Researcher Award

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Mr Zhidong CAO | Artificial Intelligence Award | Best Researcher Award

Mr Zhidong CAO, Institute of Automation, Chinese Academy of Sciences, China

Zhidong Cao 🎓 is a prominent Professor and Principal Investigator at the National Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. He holds a Doctor of Science degree from the Institute of Geographic Sciences and Natural Resources Research, CAS. With over a decade of experience at CAS, Cao has contributed significantly to AI and automation research, leading over 20 national-level projects. His scholarly impact includes 120+ papers in prestigious journals and international conferences, along with authoring 3 books. Cao has been honored with multiple awards, highlighting his substantial contributions to Chinese scientific advancement.

Publication profile

Scopus

Education

Zhidong Cao 📚 pursued a rigorous academic journey that culminated in a Ph.D. from the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences, completed from September 2005 to July 2008. Prior to his doctoral studies, he earned a Master’s degree from Changsha University of Science and Technology, spanning from September 2002 to July 2005. Cao’s educational foundation began with a Bachelor’s degree, also from Changsha University of Science and Technology, covering the period from September 1997 to July 2001. These academic milestones provided him with a comprehensive background for his subsequent influential research career in artificial intelligence and automation.

Experience