A Probabilistic Approach to Marker Propagation
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Cognitive Models. Pages 574-581.
Abstract
Pot entially, the advantages of marker-passing over local connectionist techniques for associative inference are (1) the ability to differentiate variable bindings, and (2) reduction in th e search space and/ or number of processing ele ments. However, the latter advantage has mostly been realized at the expense of accuracy and pr edictability. In this paper we consider a class of associative inference to which marker passing is often applied, variously called abductive inference, schema selection, or pattern completion. Analysis of marker semantics in a standard semantic net representation leads to a proposal for more strictly regulated marker propagation. An implementation strategy employing an augmented relaxation network is outlined.
@inproceedings{ijcai1989p92,
title = {A Probabilistic Approach to Marker Propagation},
author = {Wu, Dekai},
booktitle = {Proceedings of the Eleventh International Joint Conference on
Artificial Intelligence, {IJCAI-89}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {N. S. Sridharan},
pages = {574--581},
year = {1989},
month = {8},
note = {Cognitive Models},
url = {https://ijcai.org/proceedings/1989/92},
}
TY - CPAPER TI - A Probabilistic Approach to Marker Propagation AU - Wu, Dekai T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 574 EP - 581 UR - https://ijcai.org/proceedings/1989/92 ER -Copied
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