Embracing Causality in Specifying the Indeterminate Effects of Actions

Abstract

This paper makes the following two contributions to formal theories of actions: Showing that a causal minimization framework can be used effectively to specify the effects of indeterminate actions; and showing that for certain classes of such actions, regression, an effective computational mechanism, can be used to reason about them. Introduction Much recent work on theories of actions has concentrated on primitive, determinate actions. In this paper, we pose ourselves the problem of specifying directly the effects of indeterminate actions, 1 like we do for the primitive, determinate ones. There are several reasons why we think this is an important problem. First of all, there are actions whose effects, when described at a natural level, are indeterminate. Secondly, one can argue that there is no absolute defining line between determinate and indeterminate actions. The differences have a lot to do with the levels of descriptions. The effects of an action may be determinate at one l...

Cite

Text

Lin. "Embracing Causality in Specifying the Indeterminate Effects of Actions." AAAI Conference on Artificial Intelligence, 1996.

Markdown

[Lin. "Embracing Causality in Specifying the Indeterminate Effects of Actions." AAAI Conference on Artificial Intelligence, 1996.](https://mlanthology.org/aaai/1996/lin1996aaai-embracing/)

BibTeX

@inproceedings{lin1996aaai-embracing,
  title     = {{Embracing Causality in Specifying the Indeterminate Effects of Actions}},
  author    = {Lin, Fangzhen},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1996},
  pages     = {670-676},
  url       = {https://mlanthology.org/aaai/1996/lin1996aaai-embracing/}
}