What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Evolutionary algorithm, Mathematical optimization, Machine learning and Multi-objective optimization.
His Artificial intelligence study frequently draws connections between adjacent fields such as Heuristics.
The study incorporates disciplines such as Monte Carlo tree search, Symbolic artificial intelligence, Filter and Deep neural networks in addition to Heuristics.
The various areas that he examines in his Evolutionary algorithm study include Function and Benchmark.
His Machine learning research integrates issues from Data mining and Heuristic.
The concepts of his Multi-objective optimization study are interwoven with issues in Pareto principle, Test functions for optimization and Search algorithm.
His most cited work include:
- Planning chemical syntheses with deep neural networks and symbolic AI. (508 citations)
- A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft (273 citations)
- Sequential parameter optimization (236 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Evolutionary algorithm, Mathematical optimization, Machine learning and Evolutionary computation.
Mike Preuss undertakes interdisciplinary study in the fields of Artificial intelligence and Landscape analysis through his research.
Mike Preuss works mostly in the field of Evolutionary algorithm, limiting it down to topics relating to Cluster analysis and, in certain cases, Identification and Global optimization, as a part of the same area of interest.
His study in Function extends to Mathematical optimization with its themes.
In his study, Data mining is strongly linked to Benchmark, which falls under the umbrella field of Machine learning.
His Computational intelligence study combines topics in areas such as Game mechanics and Real-time strategy.
He most often published in these fields:
- Artificial intelligence (46.64%)
- Evolutionary algorithm (31.84%)
- Mathematical optimization (26.01%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (46.64%)
- Reinforcement learning (5.38%)
- Machine learning (24.22%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Reinforcement learning, Machine learning, Mathematical optimization and Artificial neural network.
His studies deal with areas such as Monte Carlo tree search and Computation as well as Artificial intelligence.
His Reinforcement learning research is multidisciplinary, relying on both Taxonomy and Search algorithm.
His research integrates issues of High dimensional and Robotics in his study of Machine learning.
His work in the fields of Mathematical optimization, such as Multi-objective optimization and Optimization problem, overlaps with other areas such as Focus and Ellipsoid.
His Artificial neural network study incorporates themes from Evolutionary algorithm, Baseline and Feed forward.
Between 2018 and 2021, his most popular works were:
- Orchestrating Game Generation (23 citations)
- Search Dynamics on Multimodal Multiobjective Problems (11 citations)
- From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Mike Preuss focuses on Mathematical optimization, Artificial intelligence, Focus, Machine learning and Human–computer interaction.
His work on Multi-objective optimization and Local search as part of general Mathematical optimization research is frequently linked to Selection, bridging the gap between disciplines.
Mike Preuss combines subjects such as Local optimum, Optimization problem and Global optimum with his study of Multi-objective optimization.
His Artificial intelligence study focuses on Artificial neural network in particular.
His research in Machine learning intersects with topics in Computation and Minification.
Mike Preuss has included themes like Simple, Recommender system, Feature and The Internet in his Human–computer interaction study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
