7k citations
- University of California, Santa BarbaraUS72 papers
- University of Maryland, College ParkUS21 papers
- ETH ZurichCH19 papers
- University of California, BerkeleyUS19 papers
- California Institute of TechnologyUS17 papers
- University of California, Los AngelesUS13 papers
- Board of the Swiss Federal Institutes of TechnologyCH11 papers
- Microsoft Research (United Kingdom)GB11 papers
- Princeton UniversityUS10 papers
- Stanford UniversityUS9 papers
- Oak Ridge National LaboratoryUS8 papers
- University of Tennessee at KnoxvilleUS8 papers
18 papers · 2 filters
A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
Lisa J. Burnell, Eric J. Horvitz
We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few too…
Utility-Based Abstraction and Categorization
Eric J. Horvitz, Adrian Klein
We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detaile…
Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-based Diagnosis
Sampath Srinivas, Eric J. Horvitz
The goal of model-based diagnosis is to isolate causes of anomalous system behavior and recommend inexpensive repair actions in response. In general, precomputing optimal repair po…
Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources
Eric J. Horvitz, Adrian Klein
In earlier work, we introduced flexible inference and decision-theoretic metareasoning to address the intractability of normative inference. Here, rather than pursuing the task of…
Display of Information for Time-Critical Decision Making
Eric J. Horvitz, Matthew Barry
We describe methods for managing the complexity of information displayed to people responsible for making high-stakes, time-critical decisions. The techniques provide tools for rea…
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
Dan Geiger, David Heckerman
We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by several previous authors for learning belief networks,…