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42 results on '"Nam, Wonhong"'

2. MemPatrol: Reliable Sideline Integrity Monitoring for High-Performance Systems

Author

Nam, Myoung Jin, Nam, Wonhong, Choi, Jin-Young, Akritidis, Periklis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Polychronakis, Michalis, editor, and Meier, Michael, editor

Published
2017
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6. DL2Go: Editable Digital Libraries in the Pocket

Author

Kil, Hyunyoung, Nam, Wonhong, Lee, Dongwon, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Buchanan, George, editor, Masoodian, Masood, editor, and Cunningham, Sally Jo, editor

Published
2008
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8. Learning-Based Symbolic Assume-Guarantee Reasoning with Automatic Decomposition

Author

Nam, Wonhong, Alur, Rajeev, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Graf, Susanne, editor, and Zhang, Wenhui, editor

Published
2006
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9. Symbolic Compositional Verification by Learning Assumptions

Author

Alur, Rajeev, Madhusudan, P., Nam, Wonhong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Etessami, Kousha, editor, and Rajamani, Sriram K., editor

Published
2005
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33. Synthesis and compositional verification using language learning

Author

Nam, Wonhong and Nam, Wonhong

Abstract

Formal analysis was proposed to verify correctness of complex systems. Especially, symbolic model checking using BDDs and/or S AT solvers has successfully been applied to discover subtle errors in various protocols and hardware designs, and many researches are recently being conducted for software model checking. However, many challenges are still remaining in the formal analysis field. For instance, there is some gap between real systems we want to analyze and models we can verify with the current technique, and some synthesis problems are too complex to solve efficiently. ^ Learning techniques for regular languages provide efficient algorithms to construct a minimal DFA for an unknown regular language by asking membership and/or equivalence queries to a teacher (oracle) who knows the language. Although the learning techniques are very useful to solve a number of problems, application of them has been studied relatively less since it is difficult to provide an efficient teacher who should answer the queries. ^ In this thesis, we present novel techniques using a learning algorithm in order to solve several formal analysis problems for which a DFA can be a representation for solutions. To apply the learning technique, we first define what language is the correct solution for each problem, and then we provide a teacher for the regular language. By the teacher implemented using symbolic model checking techniques, we can efficiently get the answers for queries, and construct a minimal DFA as the solution. We apply this new technique to following problems: JAVA interface synthesis, compositional verification, and safety planning problem. ^ As the first problem, we consider JAVA interface synthesis. While a typical software component, e.g. a JAVA class, has a clear documentation for static interface iv in terms of the methods and the types of input/output parameters they support, information about the correct sequences of method calls the client must invoke is usually und

Published
2007

42. Active Learning of Plans for Safety and Reachability Goals With Partial Observability.

Author

Nam, Wonhong and Alur, Rajeev

Subjects
*PLANNING, *AUTOMATION, *OBSERVABILITY (Control theory), *ACTIVE learning, *ALGORITHMS
Abstract

Traditional planning assumes reachability goals and/or full observability. In this paper, we propose a novel solution for safety and reachability planning with partial observability. Given a planning domain, a safety property, and a reachability goal, we automatically learn a safe permissive plan to guide the planning domain so that the safety property is not violated and that can force the planning domain to eventually reach states that satisfy the reachability goal, regardless of how the planning domain behaves. Our technique is based on the active learning of regular languages and symbolic model checking. The planning method first learns a safe plan using the L* algorithm, which is an efficient active learning algorithm for regular languages. We then check whether the safe plan learned is also permissive by Alternating-time Temporal Logic (ATL) model checking. If the plan is permissive, it is indeed a safe permissive plan. Otherwise, we identify and add a safe string to converge a safe permissive plan. We describe an implementation of the proposed technique and demonstrate that our tool can efficiently construct safe permissive plans for four sets of examples. [ABSTRACT FROM AUTHOR]

Published
2010
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