Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Improving the Design and Discovery of Dynamic in Sequential Decision-Making
Document related concepts
Concept learning wikipedia , lookup
Philosophy of artificial intelligence wikipedia , lookup
History of artificial intelligence wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Pattern recognition wikipedia , lookup
Time series wikipedia , lookup
Transcript
Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling Improving the Design and Discovery of Dynamic Treatment Strategies Using Recent Results in Sequential Decision-Making Joelle Pineau Reasoning and Learning Laboratory School of Computer Science McGill University Montreal, Québec, Canada In recent years, we have investigated algorithmic methods for automatically discovering and optimizing sequential treatments for chronic and life-threatening diseases. In this talk I will discuss two aspects of this work, first the problem of efficiently collecting data to learn good sequential treatment strategies, and second the problem of using data collected in multi-stage sequential trials to discover treatment strategies that are tailored to patient characteristics and timedependent outcomes. The methods will be illustrated using our recent work on learning adaptive neurostimulation policies for the treatment of epilepsy. Brief examples will be drawn from some of our other projects, including developing dynamic treatment regimes for mental illness, diabetes and cancer. c 2015, Association for the Advancement of Artificial Copyright Intelligence (www.aaai.org). All rights reserved. 373
