Joohyung Lee

Arizona State University

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Automated Reasoning about Actions

Joohyung Lee

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Citations (8)

A reductive semantics for counting and choice in answer set programming

National Conference on Artificial Intelligence (2008)

Joohyung Lee Vladimir Lifschitz Ravi Palla

In a recent paper, Ferraris, Lee and Lifschitz conjectured that the concept of a stable model of a first-order formula can be used to treat some answer set programming expressions as abbreviations. We follow up on that suggestion and introduce an answer set programming language that defines the meaning of counting and choice by reducing these constructs to first-order formulas. For the new language, the concept of a safe program is defined, and its semantic role is investigated. We compare the new language with the concept of a disjunctive program with aggregates introduced by Faber, Leone and Pfeifer, and discuss the possibility of implementing a fragment of the language by translating it into the input language of the answer set solver DLV. The language is also compared with cardinality constraint programs defined by Syrjanen.

Answer Set Programming

Cardinality (data modeling)

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Citations (49)

Recognizing Social Constructs from Textual Conversation

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2015)

Somak Aditya Chitta Baral Nguyen Vo Joohyung Lee Jieping Ye

Somak Aditya, Chitta Baral, Nguyen Ha Vo, Joohyung Lee, Jieping Ye, Zaw Naung, Barry Lumpkin, Jenny Hastings, Richard Scherl, Dawn M. Sweet, Daniela Inclezan. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

Computational linguistics

10.3115/v1/n15-1141

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Citations (1)

Representing the language of the causal calculator in answer set programming

International Conference on Logic Programming (2011)

Michael Casolary Joohyung Lee

Action languageC+, a formalism based on nonmonotonic causal logic, was designed for describing properties of actions. The definite fragment ofC+ was implemented in system the Causal Calculator (CCalc), based on a reduction of nonmonotonic causal logic to propositional logic. On the other hand, in this paper, we represent the language of CCalc in answer set programming (ASP), by translating nonmonotonic causal logic into formulas under the stable model semantics. We design a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system f2lp and answer set solvers, our prototype implementation of this approach, which we call Cplus2ASP, achieves functionality close to CCalc while taking advantage of answer set solvers to yield ecient computation that is orders of magnitude faster than CCalc on several benchmark examples. 1998 ACM Subject Classification I.2.4 Knowledge Representation Formalisms and Methods

Answer Set Programming

Non-monotonic logic

10.4230/lipics.iclp.2011.51

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Citations (20)

Self-supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-Grained Time Resolution

Lecture notes in computer science (2023)

Kwanhyung Lee John Won Heejung Hyun Sangchul Hahn Edward Choi

Predictive modelling

Regularization

10.1007/978-3-031-39539-0_4

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Citations (1)

Translating LPOD and CR-Prolog2 into Standard Answer Set Programs

EasyChair preprint (2018)

Joohyung Lee Zhun Yang

Logic Programs with Ordered Disjunction (LPOD) is an extension of standard answer set programs to handle preference using the construct of ordered disjunction,  and CR-Prolog2 is an extension of standard answer set programs with consistency restoring rules and LPOD-like ordered disjunction. We present reductions of each of these languages into the standard ASP language, which gives us an alternative way to understand the extensions in terms of the standard ASP language. 

Standard language

10.29007/d8rl

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Citations (1)

Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language $$p\mathcal{BC}{+}$$

Lecture notes in computer science (2019)

Yi Wang Joohyung Lee

Solver

Answer Set Programming

Elaboration

10.1007/978-3-030-20528-7_17

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Citations (3)

Representing Synonymity in Causal Logic and in Logic Programming

Joohyung Lee Yuliya Lierler Vladimir Lifschitz Fangkai Yang

We investigate the relationship between rules representing synonymity in nonmonotonic causal logic and in answer set programming. This question is of interest in connection with current work on modular languages for describing actions.

Answer Set Programming

Non-monotonic logic

Horn clause

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Citations (7)

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

arXiv (Cornell University) (2013)

Joohyung Lee Yunsong Meng

We present alternative definitions of the first-order stable model semantics and its extension to incorporate generalized quantifiers by referring to the familiar notion of a reduct instead of referring to the SM operator in the original definitions. Also, we extend the FLP stable model semantics to allow generalized quantifiers by referring to an operator that is similar to the $\sm$ operator. For a reasonable syntactic class of logic programs, we show that the two stable model semantics of generalized quantifiers are interchangeable.

Operator (biology)

Conservative extension

10.48550/arxiv.1301.1393

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Citations (3)

Compact and De-Biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2024)

Joohyung Lee Heejeong Nam Kwanhyung Lee Sangchul Hahn

Whole-slide image (WSI) classification is a challenging task because 1) patches from WSI lack annotation, and 2) WSI possesses unnecessary variability, e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made significant progress, allowing for classification based on slide-level, rather than patch-level, annotations. However, existing MIL methods ignore that all patches from normal slides are normal. Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches. Because our method is orthogonal to the MIL algorithm, we evaluate our method on top of the recently proposed MIL algorithms and also compare the performance with other semi-supervised approaches. We evaluate our method on two public WSI datasets including Camelyon-16 and TCGA lung cancer and demonstrate that our approach significantly improves the predictive performance of existing MIL algorithms and outperforms other semi-supervised algorithms. We release our code at https://github.com/AITRICS/pathology_mil.

Code (set theory)

10.1109/icassp48485.2024.10448245

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Citations (1)