Representing and Reasoning with Defaults for Learning Agents
Benjamin N. Grosof
AAAI-SS 1993
When we reason about change over time, causation provides an implicit preference: we prefer sequences of world states in which one world state leads causally to the next, rather than sequences in which one world state follows another at random and without causal connections. In this paper, we explore the crucial role that causation plays in our intuitions about temporal reasoning. We examine previous approaches to general temporal reasoning, and their shortcomings, in light of this analysis. We present a new system for causal reasoning, motivated action theory, which builds upon causation as a crucial preference criterion. Motivated action theory solves a broad class of temporal reasoning problems, including the traditional problems of both forward and backward reasoning, and additionally provides a basis for a new theory of explanation. © 1994.
Benjamin N. Grosof
AAAI-SS 1993
Arthur Nádas
IEEE Transactions on Neural Networks
Arnon Amir, Michael Lindenbaum
IEEE Transactions on Pattern Analysis and Machine Intelligence
Susan L. Spraragen
International Conference on Design and Emotion 2010