Eric J. Horvitz | DeepAI

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Featured Co-authors

David Heckerman
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Bart Selman
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Gregory F. Cooper
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Henry Kautz
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John S. Breese
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Paul Dagum
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Kim-Leng Poh
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Paul Koch
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∙ 07/27/2014

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 meas...

0 Eric J. Horvitz, et al. ∙

∙ 03/27/2013

The Myth of Modularity in Rule-Based Systems

In this paper, we examine the concept of modularity, an often cited adva...

0 David Heckerman, et al. ∙

∙ 03/27/2013

Reasoning About Beliefs and Actions Under Computational Resource Constraints

Although many investigators affirm a desire to build reasoning systems t...

0 Eric J. Horvitz, et al. ∙

∙ 03/27/2013

Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources

We introduce a graceful approach to probabilistic inference called bound...

0 Eric J. Horvitz, et al. ∙

∙ 03/27/2013

The Compilation of Decision Models

We introduce and analyze the problem of the compilation of decision mode...

0 David Heckerman, et al. ∙

∙ 03/27/2013

Problem Formulation as the Reduction of a Decision Model

In this paper, we extend the QMRDT probabilistic model for the domain of...

0 David Heckerman, et al. ∙

∙ 03/27/2013

Ideal Reformulation of Belief Networks

The intelligent reformulation or restructuring of a belief network can g...

0 John S. Breese, et al. ∙

∙ 03/20/2013

Time-Dependent Utility and Action Under Uncertainty

We discuss representing and reasoning with knowledge about the time-depe...

0 Eric J. Horvitz, et al. ∙

∙ 03/20/2013

An Approximate Nonmyopic Computation for Value of Information

Value-of-information analyses provide a straightforward means for select...

0 David Heckerman, et al. ∙

∙ 03/13/2013

Reformulating Inference Problems Through Selective Conditioning

We describe how we selectively reformulate portions of a belief network ...

0 Paul Dagum, et al. ∙

∙ 03/13/2013

Dynamic Network Models for Forecasting

We have developed a probabilistic forecasting methodology through a synt...

0 Paul Dagum, et al. ∙

∙ 03/06/2013

A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging

We describe the integration of logical and uncertain reasoning methods t...

0 Lisa J. Burnell, et al. ∙

∙ 03/06/2013

Reasoning about the Value of Decision-Model Refinement: Methods and Application

We investigate the value of extending the completeness of a decision mod...

0 Kim-Leng Poh, et al. ∙

∙ 03/06/2013

Utility-Based Abstraction and Categorization

We take a utility-based approach to categorization. We construct general...

0 Eric J. Horvitz, et al. ∙

∙ 02/13/2013

A Graph-Theoretic Analysis of Information Value

We derive qualitative relationships about the informational relevance of...

0 Kim-Leng Poh, et al. ∙

∙ 02/06/2013

Time-Critical Reasoning: Representations and Application

We review the problem of time-critical action and discuss a reformulatio...

0 Eric J. Horvitz, et al. ∙

∙ 02/06/2013

Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering

We describe work to control graphics rendering under limited computation...

0 Eric J. Horvitz, et al. ∙

∙ 01/30/2013

The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users

The Lumiere Project centers on harnessing probability and utility to pro...

0 Eric J. Horvitz, et al. ∙

∙ 01/30/2013

Inferring Informational Goals from Free-Text Queries: A Bayesian Approach

People using consumer software applications typically do not use technic...

0 David Heckerman, et al. ∙

∙ 01/23/2013

Attention-Sensitive Alerting

We introduce utility-directed procedures for mediating the flow of poten...

0 Eric J. Horvitz, et al. ∙

∙ 01/16/2013

Conversation as Action Under Uncertainty

Conversations abound with uncetainties of various kinds. Treating conver...

0 Tim Paek, et al. ∙

∙ 01/10/2013

A Bayesian Approach to Tackling Hard Computational Problems

We are developing a general framework for using learned Bayesian models ...

0 Eric J. Horvitz, et al. ∙

∙ 12/12/2012

Coordinates: Probabilistic Forecasting of Presence and Availability

We present methods employed in Coordinate, a prototype service that supp...

0 Eric J. Horvitz, et al. ∙

∙ 07/04/2012

Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service

We present research on developing models that forecast traffic flow and ...

0 Eric J. Horvitz, et al. ∙

∙ 06/20/2012

On Discarding, Caching, and Recalling Samples in Active Learning

We address challenges of active learning under scarce informational reso...

0 Ashish Kapoor, et al. ∙

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