Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Analogy-making as perception: a computer model
Analogy-making as perception: a computer model
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Dynamic Case Creation and Expansion for Analogical Reasoning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Automatic Classification of Containment and Support Spatial Relations in English and Dutch
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Using spatial language in multi-modal knowledge capture
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Some effects of a reduced relational vocabulary on the Whodunit problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental learning of perceptual categories for open-domain sketch recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
CogSketch: open-domain sketch understanding for cognitive science research and for education
SBM'08 Proceedings of the Fifth Eurographics conference on Sketch-Based Interfaces and Modeling
| Hi-index | 0.00 |
It is notoriously difficult to simultaneously deal with both probabilistic and structural representations in A.I., particularly because probability necessitates a uniform representation of the training examples. In this paper, we show how to build fully-specified probabilistic models from arbitrary propositional case descriptions about terrorist activities. Our method facilitates both reasoning and learning. Our solution is to use structural analogy to build probabilistic generalizations about those cases. We use these generalizations as a framework for mapping the structural representations, which are well-suited for reasoning, into features, which are well-suited for learning, and back again. Finally, we demonstrate how probabilistic generalizations are an excellent bridge for joining reasoning and learning by using them to perform a traditional machine learning technique, Bayesian network modeling, over arbitrarily high order structural data about terrorist actions, and further, we discuss how this might be used to facilitate automatic knowledge acquisition.