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1st Workshop on Logics for Reasoning About Preferences, Uncertainty, and Vagueness, PRUV 2014

作者： Lee, Joohyung Wang, Yi School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

We introduce the stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of classical propositional formulas. Combining the advantages of both formalisms, the introduced language allows highly configurable default reasoning involving fuzzy truth values. We show that several properties of Boolean stable models are naturally extended to this formalism, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.

关键词： Fuzzy logic

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Action language BC+: Preliminary report 7

Action language BC+: Preliminary report

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7th Workshop on Answer Set Programming and Other computing Paradigms, ASPOCP 2014 - Affiliated with the International Conference on Logic Programming, ICLP 2010, part of the Federated Logic Conference 2014

作者： Babb, Joseph Lee, Joohyung School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

Recently, action language BC, which combines the attractive features of action languages B and C+, was proposed. While BC allows Prolog-style recursive definitions that are not available in C+, it is less expressive than C+ in other ways, such as inability to express non-Boolean and non-exogenous actions. We propose a new action language called BC+, which encompasses all the features of BC and the definite fragment of C+. The syntax of BC+ is identical to the syntax of C+ allowing arbitrary propositional formulas in the causal laws, but its semantics is defined in terms of propositional formulas under the stable model semantics instead of nonmonotonic causal theories. This approach allows many useful ASP constructs, such as choice rules and aggregates, to be directly used in language BC+, and exploits computational methods available in ASP solvers. © ASPOCP *** rights reserved.

关键词： Semantics

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First-order extension of the FLP semantics 10

First-order extension of the FLP semantics

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10th International Symposium on Logical Formalizations of Commonsense Reasoning, Commonsense 2011

作者： Bartholomew, Michael Lee, Joohyung Meng, Yunsong School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

We provide reformulations and generalizations of both the semantics of logic programs by Faber, Leone and Pfeifer and its extension to arbitrary propositional formulas by Truszczyński. Unlike the previous definitions, our generalizations refer neither to grounding nor to fixpoints, and apply to first-order formulas containing aggregate expressions. Similar to the first-order stable model semantics by Ferraris, Lee and Lifschitz, the reformulations presented here are based on syntactic transformations that are similar to circumscription. The reformulations provide useful insights into the FLP semantics and its relationship to circumscription and the first-order stable model semantics. © 2011, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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A probabilistic extension of the stable model semantics

A probabilistic extension of the stable model semantics

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2015 AAAI Spring Symposium

作者： Lee, Joohyung Wang, Yi School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

ISBN: (纸本)9781577357087

We present a probabilistic extension of logic programs under the stable model semantics, inspired by the idea of Markov Logic Networks. The proposed language, called LPMLN, is a generalization of logic programs under the stable model semantics, and as such, embraces the rich body of research in knowledge representation. The language is also a generalization of ProbLog, and is closely related to Markov Logic Networks, which implies that the computation can be carried out by the techniques developed for them. LPMLN appears to be a natural language for probabilistic answer set programming, and as an example we show how an elaboration tolerant representation of transition systems in answer set programs can be naturally extended to the probabilistic setting. © Copyright 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Knowledge representation

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Answer Set Programming Modulo Theories and reasoning about continuous changes

Answer Set Programming Modulo Theories and reasoning about c...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Lee, Joohyung Meng, Yunsong School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

ISBN: (纸本)9781577356332

Answer Set Programming Modulo Theories is a new framework of tight integration of answer set programming (ASP) and satisfiability modulo theories (SMT). Similar to the relationship between first-order logic and SMT, it is based on a recent proposal of the functional stable model semantics by fixing interpretations of background theories. Analogously to a known relationship between ASP and SAT, "tight" ASPMT programs can be translated into SMT instances. We demonstrate the usefulness of ASPMT by enhancing action language C+ to handle continuous changes as well as discrete changes. We reformulate the semantics of C+ in terms of ASPMT, and show that SMT solvers can be used to compute the language. We also show how the language can represent cumulative effects on continuous resources.

关键词： Semantics

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Attribute-based access control for ICN naming scheme

Attribute-based access control for ICN naming scheme

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2014 IEEE Conference on Communications and Network Security, CNS 2014

作者： Li, Bing Prabhu Verleker, Ashwin Huang, Dijiang Wang, Zhijie Zhu, Yan School of Computing Informatics and Decision Systems Engineering ASU United States School of Computer and Communication Engineering USTB China

ISBN: (纸本)9781479958900

Information Centric Networking (ICN) is a new network architecture that aims to overcome the weakness of existing IP-based networking architecture. Instead of establishing a connection between the communicating hosts, ICN focuses on the content, i.e. data, transmitted in network. Content copies in ICN can be cached at different locations. The content is out of the owner's control once it's published. Thus, enforcing access control policies on distributed content copies is crucial in ICN. Attribute-Based Encryption (ABE) is a feasible approach to enforce such control mechanisms. However, applying ABE in ICN has two challenges: from management perspective, managing attributes is complicated in distributed manners;from privacy perspective, unlike in traditional networks, the enforced content access policies are public to all the ICN users. Thus, it is desirable that unauthorized content viewers are not able to retrieve the access policy. To this end, a privacy-preserving access control scheme for ICN and associated attribute management solution are presented in this paper. This proposed approach is compatible with existing flat name based ICN architectures. © 2014 IEEE.

关键词： Access control

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Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

arXiv

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arXiv 2025年

作者： Kojaku, Sadamori Mahari, Robert Lera, Sandro Claudio Moro, Esteban Pentland, Alex Ahn, Yong-Yeol Department of Systems Science and Industrial Engineering Binghamton University New York United States Media Lab Massachusetts Institute of Technology Cambridge United States Harvard Law School Cambridge United States Connection Science Massachusetts Institute of Technology Cambridge United States Southern University of Science and Technology Shenzhen China Network Science Institute Department of Physics Northeastern University Boston United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes [1–3], primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems [4–6]. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization [5, 7–14], and has helped identify key mechanisms driving these patterns [5, 15, 16]. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains. © 2025, CC BY-NC-SA.

关键词： Patents and inventions

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Visual programming language environment for different IoT and robotics platforms in computer science education

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CAAI Transactions on Intelligence Technology 2018年第2期3卷 119-130页

作者： Gennaro De Luca Zhongtao Li Sami Mian Yinong Chen School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA School of Information Science and Engineering University of Jinan Shandong People's Republic of China

This study presents the authors＇ recent research and application of a new visual programming language and its development environment： VIPLE （Visual IoT/Robotics Programming Language Environment） at Arizona State University （ASU）. ASU VIPLE supports a variety of loT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100： Introduction to engineering and in CSE 446： Software Integration and engineering, as well as in several other universities worldwide.

关键词：工作流程编程概念计算机技术人工智能

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Measuring Time-Constrained Influence to Predict Adoption in Online Social Networks

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ACM Transactions on Social computing 2020年第3期3卷 1–26页

作者： Marin, Ericsson Guo, Ruocheng Shakarian, Paulo School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States

Recently, there has been strong interest in measuring influence in online social networks. Different measures have been proposed to predict when individuals will adopt a new behavior, given the influence produced by their friends. In this article, we show that one can achieve significant improvement over these measures, extending them to consider a pair of time constraints that provide a better proxy for social influence. By conducting an engineering study that investigates retweet networks from Twitter and Sina Weibo datasets, we tune those two parameters while we examine the correlation between influence and the probability of adoption, as well as the ability to predict adoption, estimating the real susceptibility and influence to which microblog users are dynamically subjected. Although there are limitations about using retweets to analyze social influence, our results show that for the simple count of active neighbors, its correlation with the probability of adoption is boosted up to 518.75%, whereas similar gains are observed for the other influence measures analyzed. We also obtain up to 18.89% improvement in F1 score when compared to recent machine learning techniques that aim to predict adoption, enabling practical use of the corresponding concepts for social influence applications. © 2020 ACM.

关键词： Social networking (online)

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Evaluation Methods and Measures for Causal Learning Algorithms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第6期3卷 924-943页

作者： Cheng, Lu Guo, Ruocheng Moraffah, Raha Sheth, Paras Candan, K. Selcuk Liu, Huan Arizona State University School Of Computing Informatics And Decision Systems Engineering TempeAZ85287 United States City University Of Hong Kong School Of Data Science Hong Kong

The convenient access to copious multifaceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have, therefore, witnessed great effort in developing causal learning algorithms aiming to help artificial intelligence (AI) achieve human-level intelligence. Due to the lack of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with Big Data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We, then, examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of Big Data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning. © 2020 IEEE.

关键词： Maximum likelihood estimation

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