Jay C. Weber
Impact in
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- Manufacturing Process and Optimization
- Scheduling and Optimization Algorithms
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- Product Development and Customization
Papers in
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- Bayesian Modeling and Causal Inference 2
- Logic, Reasoning, and Knowledge 2
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- Distributed and Parallel Computing Systems 2
- Mobile Agent-Based Network Management 1
- Co-authors
- Jay M. Tenenbaum (3 shared papers)Thomas Gruber (2 shared papers)R. Fikes (1 shared paper)Robert S. Engelmore (1 shared paper)Mark R. Cutkosky (2 shared papers)Michael Genesereth (1 shared paper)James G. McGuire (2 shared papers)Daniel Kuokka (1 shared paper)
- Journals
- Computer (1 paper)Concurrent Engineering (1 paper)Defense Technical Information Center (DTIC) (1 paper)International Joint Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Jay C. Weber
8 papers receiving 433 citations
Peers
Comparison fields: 5 of 50
- Industrial and Manufacturing Engineering 227
- Management of Technology and Innovation 123
- Management Information Systems 99
- Artificial Intelligence 248
- Software 18
Countries citing papers authored by Jay C. Weber
This map shows the geographic impact of Jay C. Weber's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jay C. Weber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay C. Weber more than expected).
Fields of papers citing papers by Jay C. Weber
This network shows the impact of papers produced by Jay C. Weber. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jay C. Weber. The network helps show where Jay C. Weber may publish in the future.
Co-authors
The 20 scholars most cited alongside Jay C. Weber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1993 | 384 | |
| 2 | 1993 | 119 | |
| 3 | A parallel algorithm for statistical belief refinement and its use in causal reasoning | 1989 | 5 |
| 4 | 1994 | 4 | |
| 5 | 2007 | 4 | |
| 6 | Spreadsheet-like design through knowledge-based tool integration | 1992 | 4 |
| 7 | On the representation of concurrent actions in the situation calculus | 1990 | 2 |
| 8 | Principles and algorithms for causal reasoning with uncertainty | 1990 | 1 |
About Jay C. Weber
Jay C. Weber is a scholar working on Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Industrial and Manufacturing Engineering and Management Information Systems, having authored 8 papers that have together received 523 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (2 papers), Logic, Reasoning, and Knowledge (2 papers), Distributed and Parallel Computing Systems (2 papers), Manufacturing Process and Optimization (2 papers), Business Process Modeling and Analysis (1 paper), Multi-Criteria Decision Making (1 paper), Service-Oriented Architecture and Web Services (1 paper) and Mobile Agent-Based Network Management (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (227 citations), Management of Technology and Innovation (123 citations), Management Information Systems (99 citations), Artificial Intelligence (248 citations) and Software (18 citations). Jay C. Weber has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Jay M. Tenenbaum, Thomas Gruber, R. Fikes, Robert S. Engelmore, Mark R. Cutkosky, Michael Genesereth, James G. McGuire, Daniel Kuokka, Gregory R. Olsen and Richard N. Pelavin. Their work appears in journals such as Computer, Concurrent Engineering, Defense Technical Information Center (DTIC) and International Joint Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.