Model Selection for Solving Kinematics Problems
There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the mod...
| Main Author: | |
|---|---|
| Language: | en_US |
| Published: |
2004
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/6816 |
| _version_ | 1811073808593846272 |
|---|---|
| author | Goh, Choon P. |
| author_facet | Goh, Choon P. |
| author_sort | Goh, Choon P. |
| collection | MIT |
| description | There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the models come from? How do we know we have the right models? What does the right model mean anyway? Our work has three major components. The first component deals with how we determine whether a piece of information is relevant to solving a problem. We have three ways of determining relevance: derivational, situational and an order-of-magnitude reasoning process. The second component deals with the defining and building of models for solving problems. We identify these models, determine what we need to know about them, and importantly, determine when they are appropriate. Currently, the system has a collection of four basic models and two hybrid models. This collection of models has been successfully tested on a set of fifteen simple kinematics problems. The third major component of our work deals with how the models are selected. |
| first_indexed | 2024-09-23T09:39:07Z |
| id | mit-1721.1/6816 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:39:07Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/68162019-04-12T08:32:23Z Model Selection for Solving Kinematics Problems Goh, Choon P. Canonical Models model selection lineau kinematics sdetermine relevance equation generation There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the models come from? How do we know we have the right models? What does the right model mean anyway? Our work has three major components. The first component deals with how we determine whether a piece of information is relevant to solving a problem. We have three ways of determining relevance: derivational, situational and an order-of-magnitude reasoning process. The second component deals with the defining and building of models for solving problems. We identify these models, determine what we need to know about them, and importantly, determine when they are appropriate. Currently, the system has a collection of four basic models and two hybrid models. This collection of models has been successfully tested on a set of fifteen simple kinematics problems. The third major component of our work deals with how the models are selected. 2004-10-20T19:58:01Z 2004-10-20T19:58:01Z 1990-09-01 AITR-1257 http://hdl.handle.net/1721.1/6816 en_US AITR-1257 91 p. 9434297 bytes 3566163 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Canonical Models model selection lineau kinematics sdetermine relevance equation generation Goh, Choon P. Model Selection for Solving Kinematics Problems |
| title | Model Selection for Solving Kinematics Problems |
| title_full | Model Selection for Solving Kinematics Problems |
| title_fullStr | Model Selection for Solving Kinematics Problems |
| title_full_unstemmed | Model Selection for Solving Kinematics Problems |
| title_short | Model Selection for Solving Kinematics Problems |
| title_sort | model selection for solving kinematics problems |
| topic | Canonical Models model selection lineau kinematics sdetermine relevance equation generation |
| url | http://hdl.handle.net/1721.1/6816 |
| work_keys_str_mv | AT gohchoonp modelselectionforsolvingkinematicsproblems |