Using Machine Learning for Decreasing State Uncertainty in Planning
نویسندگان
چکیده
منابع مشابه
Decreasing Uncertainty in Planning with State Prediction
In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machi...
متن کاملReservoir Uncertainty Assessment Using Machine Learning Techniques
Petroleum exploration and production are associated with great risk because of the uncertainty on subsurface conditions. Understanding the impact of those uncertainties on the production performance is a crucial part in the decision making process.Traditionally, uncertainty assessment is performed using experimental design and response surface method, in which a number of training points are se...
متن کاملIdentifying Students' Inquiry Planning Using Machine Learning
This research investigates the detection of student meta-cognitive planning processes in real-time using log tracing techniques. We use fine and coarse-grained data distillation, in combination with coarse-grained text replay coding, in order to develop detectors for students’ planning of experiments in Science Assistments, an assessment and tutoring system for scientific inquiry. The goal is t...
متن کاملMachine Learning for Adaptive Planning
This chapter is concerned with the enhancement of planning systems using techniques from Machine Learning in order to automatically configure their planning parameters according to the morphology of the problem in hand. It presents two different adaptive systems that set the planning parameters of a highly adjustable planner based on measurable characteristics of the problem instance. The plann...
متن کاملRobust Optimization using Machine Learning for Uncertainty Sets
Our goal is to build robust optimization problems for making decisions based on complex data from the past. In robust optimization (RO) generally, the goal is to create a policy for decision-making that is robust to our uncertainty about the future. In particular, we want our policy to best handle the the worst possible situation that could arise, out of an uncertainty set of possible situation...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2020
ISSN: 1076-9757
DOI: 10.1613/jair.1.11567