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Citation: T. K. Satish Kumar. (2000) Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional Modeling. In Proceedings of the Fourteenth International Workshop on Qualitative Reasoning, 2000.
| Publication inproceedings ( Edit )
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| type | InProceedings
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| bibtype | inproceedings
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| Bibtex basics
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| author | T. K. Satish Kumar
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| title | Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional Modeling
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| booktitle | Proceedings of the Fourteenth International Workshop on Qualitative Reasoning
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| address | Mexico
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| year | 2000
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| abstract | Compositional modeling provides a number of advantages over conventional simulation software in explanation generation mainly because of its causal interpretation of data. However, little work was done with regard to a supporting algorithm that can generate cogent explanations from the simulation values and causal graphs of model parameters. Earlier attempts did not solve the problem of irrelevant details introduced by using compositional modeling; as a result of which misleading references resulted in attempting explanation of device behavior. This was mainly because they were based merely on equation tracing and did not try to infer anything about the working phenomena from the causal order graph. We presents domain independent algorithm that interprets causal order graphs in terms of working template phenomena rather than in terms of quantities defined in the equation model. A byproduct of this is in capturing the user's psychology in terms of phenomena rather than in terms of mathematical equations defined by some other person. The explanation is in the form of natural language rather than graphs of numerical variables. We also describe a number of extensions of the algorithm to handle issues such as scalability and ranking by significance.
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| KSL Technical Report ID: KSL-00-03
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Facts about Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional ModelingRDF feed
| Abstract | Compositional modeling provides a number o … Compositional modeling provides a number of advantages over conventional simulation software in explanation generation mainly because of its causal interpretation of data. However, little work was done with regard to a supporting algorithm that can generate cogent explanations from the simulation values and causal graphs of model parameters. Earlier attempts did not solve the problem of irrelevant details introduced by using compositional modeling; as a result of which misleading references resulted in attempting explanation of device behavior. This was mainly because they were based merely on equation tracing and did not try to infer anything about the working phenomena from the causal order graph. We presents domain independent algorithm that interprets causal order graphs in terms of working template phenomena rather than in terms of quantities defined in the equation model. A byproduct of this is in capturing the user's psychology in terms of phenomena rather than in terms of mathematical equations defined by some other person. The explanation is in the form of natural language rather than graphs of numerical variables. We also describe a number of extensions of the algorithm to handle issues such as scalability and ranking by significance. scalability and ranking by significance. |
| Address | Mexico + |
| Author | T. K. Satish Kumar + |
| Bibtype | inproceedings + |
| Booktitle | Proceedings of the Fourteenth International Workshop on Qualitative Reasoning + |
| Has author | T. K. Satish Kumar + |
| Has identifier | KSL-00-03 + |
| Has publishing details | 2000 + |
| Has title | Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional Modeling + |
| Has where published | Proceedings of the Fourteenth International Workshop on Qualitative Reasoning + |
| Has year | 2000 + |
| Ksl tr id | KSL-00-03 + |
| Process note | NO + |
| Title | Reinterpretation of Causal Order Graphs Towards Effective Explanation Generation Using Compositional Modeling + |
| Year | 2000 + |
