Generalizing Problem Reduction: A Logical Analysis
Proceedings of the Eighth International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 302-307.
Abstract
Problem reduction is the name given to the problemsolving paradigm in which the problem solver manages a network of "tasks" representing its intentions, repeatedly reducing tasks to subtasks and coordinating their execution. This idea needs a lot of generalization for it to be able to ha ndle a realistic range of problems. Even after the model of time is made more realistic (to handle continuity and bra nching), issues remain regarding what it means to have a ta sk or a subtask, how a task can succeed or fail, whether a t,ask is feasib le. A profitable way to study these issues is to attempt to add axioms about tasks to a first-order temporal logic . The result sheds light on what sorts of generalizations of task networks are needed.
@inproceedings{ijcai1983p71,
title = {Generalizing Problem Reduction: A Logical Analysis},
author = {McDermott, Drew V.},
booktitle = {Proceedings of the Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-83}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Alan Bundy},
pages = {302--307},
year = {1983},
note = {Knowledge Representation},
url = {https://ijcai.org/proceedings/1983/71},
}
TY - CPAPER TI - Generalizing Problem Reduction: A Logical Analysis AU - McDermott, Drew V. T2 - Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI-83 PB - International Joint Conferences on Artificial Intelligence PY - 1983 SP - 302 EP - 307 UR - https://ijcai.org/proceedings/1983/71 ER -Copied
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