What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- Algorithm
Artificial intelligence, Hierarchical task network, Plan, Automated planning and scheduling and Operations research are her primary areas of study.
Her Artificial intelligence research integrates issues from Set, Task and Decomposition.
Her work deals with themes such as Semantics, Programming language, Web service and Management science, which intersect with Hierarchical task network.
Her research integrates issues of Model checking and Planning Domain Definition Language in her study of Management science.
Her Plan study combines topics in areas such as Quality, New product development and Set.
Much of her study explores Automated planning and scheduling relationship to Control.
Her most cited work include:
- Automated Planning, Theory And Practice (1394 citations)
- Automated Planning: Theory & Practice (849 citations)
- SHOP2: an HTN planning system (795 citations)
What are the main themes of her work throughout her whole career to date?
Dana S. Nau mainly investigates Artificial intelligence, Theoretical computer science, Plan, Hierarchical task network and Planner.
Dana S. Nau has included themes like Domain, Set, Task, Machine learning and Game tree in her Artificial intelligence study.
Her Theoretical computer science research is multidisciplinary, relying on both Algorithm and Representation.
Her Plan research integrates issues from Quality, Process, Design for manufacturability and Operations research.
Her study looks at the intersection of Hierarchical task network and topics like Automated planning and scheduling with Integer programming and Management science.
Her work deals with themes such as Planning algorithms and Computation, which intersect with Planner.
She most often published in these fields:
- Artificial intelligence (34.89%)
- Theoretical computer science (13.46%)
- Plan (12.09%)
What were the highlights of her more recent work (between 2010-2021)?
- Artificial intelligence (34.89%)
- Game theory (5.22%)
- Norm (2.20%)
In recent papers she was focusing on the following fields of study:
Dana S. Nau mainly focuses on Artificial intelligence, Game theory, Norm, Mathematical optimization and Theoretical computer science.
Her Artificial intelligence study combines topics in areas such as Hierarchical task network, Human–computer interaction, Monte Carlo tree search, Machine learning and Game tree.
Her Hierarchical task network research focuses on Domain knowledge and how it relates to Planning algorithms.
Her Mathematical optimization research incorporates elements of Simple and Euclidean space.
Her Theoretical computer science research includes themes of Correctness and Distributive property.
She interconnects Automated planning and scheduling and Management science in the investigation of issues within Formalism.
Between 2010 and 2021, her most popular works were:
- Automated Planning and Acting (133 citations)
- Societal threat and cultural variation in the strength of social norms: An evolutionary basis (99 citations)
- Social Computing, Behavioral-Cultural Modeling and Prediction (88 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
Her main research concerns Artificial intelligence, Norm, Planner, Human–computer interaction and Heuristics.
Her Hierarchical task network research extends to Artificial intelligence, which is thematically connected.
Her studies in Hierarchical task network integrate themes in fields like Planning Domain Definition Language, Domain model, Goal decomposition and Domain knowledge.
Her Norm study integrates concerns from other disciplines, such as Microeconomics and Evolutionary game theory.
Her Planner research incorporates themes from Motion control, Gödel, Mobile robot and Operations research.
Her research investigates the connection with Heuristics and areas like Hybrid system which intersect with concerns in Mathematical optimization.
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.
