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Found 11 papers, 0 papers with code
The Compilation of Decision Models
We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective.
Exact Reasoning Under Uncertainty
This paper focuses on designing expert systems to support decision making in complex, uncertain environments.
Interval Influence Diagrams
We describe a mechanism for performing probabilistic reasoning in influence diagrams using interval rather than point valued probabilities.
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.
Decision-Theoretic Control of Problem Solving: Principles and Architecture
This paper presents an approach to the design of autonomous, real-time systems operating in uncertain environments.
Integrating Logical and Probabilistic Reasoning for Decision Making
We describe a representation and a set of inference methods that combine logic programming techniques with probabilistic network representations for uncertainty (influence diagrams).
Decision Making with Interval Influence Diagrams
The output of the algorithm are a set of admissible alternatives for each decision variable and a set of bounds on expected value based on the imprecision in the input.
IDEAL: A Software Package for Analysis of Influence Diagrams
IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software environment for creation and evaluation of belief networks and influence diagrams.
A New Look at Causal Independence
In this representation, the interaction between causes and effect can be written as a nested decomposition of functions.
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
We develop and extend existing decision-theoretic methods for troubleshooting a nonfunctioning device.
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods.
