The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.
The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.
Haddawy, Peter, Hanks, Steve
The Association for the Advancement of Artificial Intelligence Spring Symposium on Interactive and Mixed-Initiative Decision-Theoretic Systems was held at Stanford University from 23-25 March 1998. The symposium attracted approximately 30 researchers from around the world. Topics discussed included incremental model construction, user interaction, explanation generation, and applications.