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Found 10 papers, 1 papers with code
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e. g., web pages).
Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research
Over the last decade, there has been growing interest in the use or measures or change in belief for reasoning with uncertainty in artificial intelligence research.
Reasoning About Beliefs and Actions Under Computational Resource Constraints
Although many investigators affirm a desire to build reasoning systems that behave consistently with the axiomatic basis defined by probability theory and utility theory, limited resources for engineering and computation can make a complete normative analysis impossible.
The Myth of Modularity in Rule-Based Systems
However, we argue that in the case of plausible reasoning, rules are syntactically modular but are rarely semantically modular.
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
We introduce a graceful approach to probabilistic inference called bounded conditioning.
The Compilation of Decision Models
We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective.
Ideal Reformulation of Belief Networks
Thus, under time pressure, there is a tradeoff between the time dedicated to reformulating the network and the time applied to the implementation of a solution.
Problem Formulation as the Reduction of a Decision Model
In this paper, we extend the QMRDT probabilistic model for the domain of internal medicine to include decisions about treatments.
An Approximate Nonmyopic Computation for Value of Information
Value-of-information analyses provide a straightforward means for selecting the best next observation to make, and for determining whether it is better to gather additional information or to act immediately.
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
People using consumer software applications typically do not use technical jargon when querying an online database of help topics.
