What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Artificial intelligence, Machine learning, Instance-based learning, Case-based reasoning and Algorithm.
His Artificial intelligence study integrates concerns from other disciplines, such as Task and Set.
He combines subjects such as Class, Data mining and Collective classification with his study of Machine learning.
His research investigates the connection between Instance-based learning and topics such as Feature that intersect with problems in Population-based incremental learning and Constructive induction.
His Algorithm research integrates issues from Semi-supervised learning and k-nearest neighbors algorithm.
David W. Aha focuses mostly in the field of Decision tree, narrowing it down to matters related to Data structure and, in some cases, Decision tree learning.
His most cited work include:
- Instance-Based Learning Algorithms (4024 citations)
- A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms (638 citations)
- Lazy learning (381 citations)
What are the main themes of his work throughout his whole career to date?
David W. Aha mainly investigates Artificial intelligence, Machine learning, Case-based reasoning, Task and Human–computer interaction.
The various areas that David W. Aha examines in his Artificial intelligence study include Domain, Algorithm, Pattern recognition and Natural language processing.
His study in Algorithm is interdisciplinary in nature, drawing from both Set, Feature selection and k-nearest neighbors algorithm.
His Machine learning study combines topics from a wide range of disciplines, such as Data mining and Collective classification.
David W. Aha interconnects Management science, Knowledge management, Reasoning system, Decision support system and Model-based reasoning in the investigation of issues within Case-based reasoning.
His research investigates the link between Reasoning system and topics such as Qualitative reasoning that cross with problems in Adaptive reasoning.
He most often published in these fields:
- Artificial intelligence (55.47%)
- Machine learning (24.22%)
- Case-based reasoning (19.53%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (55.47%)
- Human–computer interaction (13.67%)
- Machine learning (24.22%)
In recent papers he was focusing on the following fields of study:
David W. Aha mainly focuses on Artificial intelligence, Human–computer interaction, Machine learning, Task and Goal reasoning.
The concepts of his Artificial intelligence study are interwoven with issues in Domain and Computer vision.
His work in Human–computer interaction covers topics such as Multi-agent system which are related to areas like Data mining.
In his research, Collective classification is intimately related to Statistical relational learning, which falls under the overarching field of Machine learning.
His research integrates issues of Autonomous agent and Management science in his study of Goal reasoning.
His Heuristics study incorporates themes from Algorithm, Domain model, Procedural knowledge and Heuristic.
Between 2012 and 2020, his most popular works were:
- DARPA’s Explainable Artificial Intelligence (XAI) Program (91 citations)
- Goal-Driven Autonomy for Responding to Unexpected Events in Strategy Simulations (45 citations)
- Multiparticipant chat analysis: a survey (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His scientific interests lie mostly in Artificial intelligence, Task, Goal reasoning, Hierarchical task network and Robotics.
His Artificial intelligence research includes themes of Domain, Machine learning and Empirical research.
David W. Aha integrates Machine learning with Error tolerance in his research.
His Task study combines topics from a wide range of disciplines, such as Applications of artificial intelligence, Ai systems and End user.
His research integrates issues of Qualitative reasoning, Management science, Representation, Human–computer interaction and Goal orientation in his study of Goal reasoning.
His Robotics study integrates concerns from other disciplines, such as Iterative refinement, Relation, Knowledge management and Waypoint.
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