Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
AIIM Journal 2023 Journal Article
TIST Journal 2012 Journal Article
We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.
IS Journal 2009 Journal Article
Interest in and requirements for the next generation of information technology for science are expanding. e-Science has become a growing subject of discussion covering topics such as grid computing for science as well as knowledge-enhanced scientific data retrieval. The demand for deep integration of scientific data and knowledge within and among disciplines is also growing, as larger and broader science questions are becoming more common. Concurrent with the growing demand for next generation information technology for science is a commensurate growth in semantic technologies. We seek to explore the general space of semantic e-Science while focusing on the support and potential for deep data and knowledge integration.
AAAI Conference 2007 System Paper
AAAI Conference 1998 Conference Paper
The amount of use a knowledge representation system receives depends on more than just the theoretical suitability of the system. Some critical determiners of usage have to do with issues related to the representation formalism of the system, some have to do with non-representational issues of the system itself, and some might be most appropriately labeled public relations. We rely on over eight years of industrial application experiences using a particular family of knowledge representation systems based on description logics to identify and describe usability issues that were mandatory for our application successes.