Hypothesizing and Refining Causal Models
An important common sense competence is the ability to hypothesize causal relations. This paper presents a set of constraints which make the problem of formulating causal hypotheses about simple physical systems a tractable one. The constraints include: (1) a temporal and physical proximity r...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/6417 |
| _version_ | 1826203345255137280 |
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| author | Doyle, Richard J. |
| author_facet | Doyle, Richard J. |
| author_sort | Doyle, Richard J. |
| collection | MIT |
| description | An important common sense competence is the ability to hypothesize causal relations. This paper presents a set of constraints which make the problem of formulating causal hypotheses about simple physical systems a tractable one. The constraints include: (1) a temporal and physical proximity requirement, (2) a set of abstract causal explanations for changes in physical systems in terms of dependences between quantities, and (3) a teleological assumption that dependences in designed physical systems are functions. These constraints were embedded in a learning system which was tested in two domains: a sink and a toaster. The learning system successfully generated and refined naﶥ causal models of these simple physical systems. The causal models which emerge from the learning process support causal reasoning- explanation, prediction, and planning. Inaccurate predictions and failed plans in turn indicate deficiencies in the causal models and the need to re-hypothesize. Thus learning supports reasoning which leads to further learning. The learning system makes use of standard inductive rules of inference as well as the constraints on causal hypotheses to generalize its causal models. Finally, a simple example involving an analogy illustrates another way to repair incomplete causal models. |
| first_indexed | 2024-09-23T12:35:08Z |
| id | mit-1721.1/6417 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:35:08Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/64172019-04-12T08:30:38Z Hypothesizing and Refining Causal Models Doyle, Richard J. An important common sense competence is the ability to hypothesize causal relations. This paper presents a set of constraints which make the problem of formulating causal hypotheses about simple physical systems a tractable one. The constraints include: (1) a temporal and physical proximity requirement, (2) a set of abstract causal explanations for changes in physical systems in terms of dependences between quantities, and (3) a teleological assumption that dependences in designed physical systems are functions. These constraints were embedded in a learning system which was tested in two domains: a sink and a toaster. The learning system successfully generated and refined naﶥ causal models of these simple physical systems. The causal models which emerge from the learning process support causal reasoning- explanation, prediction, and planning. Inaccurate predictions and failed plans in turn indicate deficiencies in the causal models and the need to re-hypothesize. Thus learning supports reasoning which leads to further learning. The learning system makes use of standard inductive rules of inference as well as the constraints on causal hypotheses to generalize its causal models. Finally, a simple example involving an analogy illustrates another way to repair incomplete causal models. 2004-10-04T14:55:43Z 2004-10-04T14:55:43Z 1984-12-01 AIM-811 http://hdl.handle.net/1721.1/6417 en_US AIM-811 12954951 bytes 10216960 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Doyle, Richard J. Hypothesizing and Refining Causal Models |
| title | Hypothesizing and Refining Causal Models |
| title_full | Hypothesizing and Refining Causal Models |
| title_fullStr | Hypothesizing and Refining Causal Models |
| title_full_unstemmed | Hypothesizing and Refining Causal Models |
| title_short | Hypothesizing and Refining Causal Models |
| title_sort | hypothesizing and refining causal models |
| url | http://hdl.handle.net/1721.1/6417 |
| work_keys_str_mv | AT doylerichardj hypothesizingandrefiningcausalmodels |