What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Human–computer interaction, Game mechanics, Game design and Preference learning.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Entertainment.
His studies deal with areas such as User interface, Multimedia, Novelty and Computer-aided as well as Human–computer interaction.
His study in the field of Video game design is also linked to topics like sort and Navigation mesh.
His study looks at the relationship between Game design and fields such as Creativity, as well as how they intersect with chemical problems.
His Preference learning research is multidisciplinary, incorporating elements of Affective computing, Frustration and Pairwise comparison.
His most cited work include:
- Search-Based Procedural Content Generation: A Taxonomy and Survey (472 citations)
- Experience-Driven Procedural Content Generation (310 citations)
- Player modeling using self-organization in Tomb Raider: Underworld (210 citations)
What are the main themes of his work throughout his whole career to date?
Georgios N. Yannakakis mainly investigates Artificial intelligence, Human–computer interaction, Multimedia, Game design and Machine learning.
His study in Preference learning extends to Artificial intelligence with its themes.
His work deals with themes such as Scheme and Creativity, which intersect with Human–computer interaction.
His study in Multimedia is interdisciplinary in nature, drawing from both Field and Entertainment.
His research in the fields of Game design document and Game Developer overlaps with other disciplines such as Non-cooperative game and Simultaneous game.
His Machine learning research includes themes of Novelty, Surprise, Robustness and Search algorithm.
He most often published in these fields:
- Artificial intelligence (41.90%)
- Human–computer interaction (26.76%)
- Multimedia (18.66%)
What were the highlights of his more recent work (between 2017-2021)?
- Human–computer interaction (26.76%)
- Artificial intelligence (41.90%)
- Affect (8.45%)
In recent papers he was focusing on the following fields of study:
His main research concerns Human–computer interaction, Artificial intelligence, Affect, Deep learning and Cognitive psychology.
His Human–computer interaction research includes elements of Domain, Observer, Preference learning and Cognitive reframing.
His Artificial intelligence research incorporates elements of Field, Machine learning and Novelty.
The concepts of his Field study are interwoven with issues in Multimedia, Ai education and Computational thinking.
His work on Game design as part of general Multimedia research is often related to Content generation and Computational Science and Engineering, thus linking different fields of science.
His research integrates issues of Annotation, Key and Perception in his study of Affect.
Between 2017 and 2021, his most popular works were:
- Artificial Intelligence and Games (76 citations)
- Procedural Content Generation through Quality Diversity (33 citations)
- The Ordinal Nature of Emotions: An Emerging Approach (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Georgios N. Yannakakis mainly focuses on Artificial intelligence, Deep learning, Affective computing, Human–computer interaction and Task analysis.
He regularly ties together related areas like Machine learning in his Artificial intelligence studies.
His studies in Machine learning integrate themes in fields like Novelty, Surprise and Robustness.
Georgios N. Yannakakis has included themes like Game design, Multimedia, Field and Surrogate model in his Deep learning study.
His Affective computing study integrates concerns from other disciplines, such as Cognitive psychology, Key and Preference learning.
His Human–computer interaction research is multidisciplinary, relying on both Domain, Valence, Crowdsourcing and Affect.
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