What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Scheduling, Heuristics, Artificial intelligence, Scheduling and Mathematical optimization.
His study looks at the relationship between Scheduling and topics such as Metaheuristic, which overlap with Brute-force search and Choice function.
His Heuristics research is multidisciplinary, relying on both Tabu search, Industrial engineering, Heuristic, Simulation and Job shop scheduling.
His Artificial intelligence research incorporates themes from Monte Carlo tree search, Machine learning and Game theory.
His studies in Game theory integrate themes in fields like Decision theory and Computer Go.
His study in Scheduling is interdisciplinary in nature, drawing from both Hot strip mill, Real-time computing and Dynamic priority scheduling.
His most cited work include:
- A Survey of Monte Carlo Tree Search Methods (1543 citations)
- A hyperheuristic approach to scheduling a sales summit (264 citations)
- A Memetic Approach to the Nurse Rostering Problem (201 citations)
What are the main themes of his work throughout his whole career to date?
Peter I. Cowling mainly investigates Artificial intelligence, Mathematical optimization, Machine learning, Heuristics and Monte Carlo tree search.
His Artificial intelligence study combines topics in areas such as Ranking, Tree, Data mining and Pattern recognition.
His work in Mathematical optimization tackles topics such as Scheduling which are related to areas like Operations research and Combinatorial optimization.
His Fuzzy set research extends to the thematically linked field of Machine learning.
He interconnects Genetic algorithm, Nurse scheduling problem, Heuristic and Job shop scheduling in the investigation of issues within Heuristics.
His research in Monte Carlo tree search intersects with topics in Game tree, Perfect information and Game theory.
He most often published in these fields:
- Artificial intelligence (34.93%)
- Mathematical optimization (23.97%)
- Machine learning (17.81%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (34.93%)
- Monte Carlo tree search (14.38%)
- Mathematical optimization (23.97%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Monte Carlo tree search, Mathematical optimization, Knowledge management and Human–computer interaction.
The study incorporates disciplines such as Machine learning, Sequential game, Outcome and Key in addition to Artificial intelligence.
His work on Association rule learning as part of general Machine learning study is frequently linked to Strategic advantage, bridging the gap between disciplines.
His Monte Carlo tree search research includes elements of Game tree, Domain knowledge and Implementation.
His work in the fields of Mathematical optimization, such as Metaheuristic, intersects with other areas such as Binary number and Neighbourhood.
His study in the fields of Video game design under the domain of Human–computer interaction overlaps with other disciplines such as Congruence.
Between 2014 and 2020, his most popular works were:
- From value chains to technological platforms: The effects of crowdfunding in the digital game industry (44 citations)
- Predicting player disengagement and first purchase with event-frequency based data representation (40 citations)
- Exploring the relationship between video game expertise and fluid intelligence. (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Artificial intelligence, Factory, Monte Carlo tree search, Multimedia and Battle.
He has included themes like Machine learning, Logistic regression, Cognitive resource theory and Simultaneous game in his Artificial intelligence study.
His work in the fields of Multivariate statistics and Random forest overlaps with other areas such as Variance and Term.
His studies deal with areas such as Artificial neural network and Implementation as well as Monte Carlo tree search.
Multimedia is connected with Flexibility, Graphics, Mainstream, Storytelling and Echo in his research.
His Key course of study focuses on Data science and Predictive modelling and Feature engineering.
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