Joohyung Lee

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Arizona State University

Strong Equivalence for LPMLN Programs

LPMLN is a probabilistic extension of answer set programs with the weigh...
09/18/2019 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

To be responsive to dynamically changing real-world environments, an int...
07/31/2019 ∙ by Yi Wang, et al. ∙ 0 ∙ share

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Explainable Fact Checking with Probabilistic Answer Set Programming

One challenge in fact checking is the ability to improve the transparenc...
06/21/2019 ∙ by Naser Ahmadi, et al. ∙ 0 ∙ share

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Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+

We extend probabilistic action language pBC+ with the notion of utility ...
04/01/2019 ∙ by Yi Wang, et al. ∙ 0 ∙ share

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Fully Convolutional Network-based Multi-Task Learning for Rectum and Rectal Cancer Segmentation

In this study, we present a fully automatic method to segment both rectu...
01/22/2019 ∙ by Joohyung Lee, et al. ∙ 6 ∙ share

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Multi-Task Learning with a Fully Convolutional Network for Rectum and Rectal Cancer Segmentation

In a rectal cancer treatment planning, the location of rectum and rectal...
01/22/2019 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Weight Learning in a Probabilistic Extension of Answer Set Programs

LPMLN is a probabilistic extension of answer set programs with the weigh...
08/14/2018 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Translating LPOD and CR-Prolog2 into Standard Answer Set Programs

Logic Programs with Ordered Disjunction (LPOD) is an extension of standa...
05/02/2018 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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A Probabilistic Extension of Action Language BC+

We present a probabilistic extension of action language BC+. Just like B...
05/02/2018 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Representing Hybrid Automata by Action Language Modulo Theories

Both hybrid automata and action languages are formalisms for describing ...
07/20/2017 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Computing LPMLN Using ASP and MLN Solvers

LPMLN is a recent addition to probabilistic logic programming languages....
07/19/2017 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic

Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widel...
06/28/2016 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming

Circumscription and logic programs under the stable model semantics are ...
01/18/2014 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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First-Order Stable Model Semantics and First-Order Loop Formulas

Lin and Zhaos theorem on loop formulas states that in the propositional ...
01/16/2014 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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A Functional View of Strong Negation in Answer Set Programming

The distinction between strong negation and default negation has been us...
12/20/2013 ∙ by Michael Bartholomew, et al. ∙ 0 ∙ share

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Two New Definitions of Stable Models of Logic Programs with Generalized Quantifiers

We present alternative definitions of the first-order stable model seman...
01/08/2013 ∙ by Joohyung Lee, et al. ∙ 0 ∙ share

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Module Theorem for The General Theory of Stable Models

The module theorem by Janhunen et al. demonstrates how to provide a modu...
10/18/2012 ∙ by Joseph Babb, et al. ∙ 0 ∙ share

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Representing First-Order Causal Theories by Logic Programs

Nonmonotonic causal logic, introduced by Norman McCain and Hudson Turner...
03/23/2011 ∙ by Paolo Ferraris, et al. ∙ 0 ∙ share

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On Elementary Loops of Logic Programs

Using the notion of an elementary loop, Gebser and Schaub refined the th...
12/28/2010 ∙ by Martin Gebser, et al. ∙ 0 ∙ share

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Joohyung Lee

Featured Co-authors

Yi Wang

Shiqi Zhang

Martin Gebser

Yuliya Lierler

Fangkai Yang

Vladimir Lifschitz

Man Luo

Paolo Papotti

Yunsong Meng

Min Ju Kim

Bo Yun Hur

Strong Equivalence for LPMLN Programs

Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

Explainable Fact Checking with Probabilistic Answer Set Programming

Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+

Fully Convolutional Network-based Multi-Task Learning for Rectum and Rectal Cancer Segmentation

Multi-Task Learning with a Fully Convolutional Network for Rectum and Rectal Cancer Segmentation

Weight Learning in a Probabilistic Extension of Answer Set Programs

Translating LPOD and CR-Prolog2 into Standard Answer Set Programs

A Probabilistic Extension of Action Language BC+

Representing Hybrid Automata by Action Language Modulo Theories

Computing LPMLN Using ASP and MLN Solvers

On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic

Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming

First-Order Stable Model Semantics and First-Order Loop Formulas

A Functional View of Strong Negation in Answer Set Programming

Two New Definitions of Stable Models of Logic Programs with Generalized Quantifiers

Module Theorem for The General Theory of Stable Models

Representing First-Order Causal Theories by Logic Programs

On Elementary Loops of Logic Programs