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Liau, Churn-Jung ޼Ӻᔊʧ Selected Publications
Selected Publications
޼Ӻᔊʧ
(A) Edited Volumes: (C) Springer-Verlag LNCS/LNAI series:
Ңٙ޼Ӻጳሳί׵ଣ׌ၾٝᗆ͉ٙሯʘઞীd
1. T.Y. Lin, S. Ohsuga, C.J. Liau, and X. Hu (2006), Foundations and 1. Y.T. Chiang, D.W. Wang, C.J. Liau, and T.-s. Hsu (2005), Secrecy of
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ʿՉίɛʈ౽ᅆ˙̙ࠦٙঐᏐ͜fҢࡁ˴ࠅઞীɓ Novel Approaches in Data Mining, Springer-Verlag. two-party secure computation, Proc. of the 19 Annual IFIP WG 11.3
2. T.Y. Lin, S. Ohsuga, C.J. Liau, X. Hu, and S. Tsumoto (2005), Foun- Working Conference on Data and Applications Security, 114-123,
ࡈଣ׌ٙ˴᜗νОᐏ՟ٝᗆdνО˸ᜌ፨˙جڌ༺ dations of Data Mining and Knowledge Discovery, Springer-Verlag. LNCS 3654.
3. T.Y. Lin, S. Smale, T. Poggio, and C.J. Liau (2004), Proc. of the IEEE 2. C.J. Liau (2005), Ordered belief fusion in possibilistic logic, Proc.
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ՉٝᗆdԨ຾͟ᜌ፨પଣਂ̈Ӕഄʿમ՟Бਗٙཀ ICDM’04 Workshop on Foundations of Data Mining. of the 10 International Conference on Rough Sets, Fuzzy Sets, Data
4. T.Y. Lin, X. Hu, S. Ohsuga, and C.J. Liau (2003), Proc. of the IEEE Mining, and Granular Computing, pp. 632-641, LNAI 3641.
೻fतй݊ί༟ৃʔ̂ʱʿʔҁΌٙᐑྤɨdՉᜌ ICDM’03 Workshop on Foundations and New Directions in Data Min- 3. C.J. Liau (2004), Belief reasoning, revision, and fusion by matrix
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ing. algebra, Proc. of the 4 International Conference on Rough Sets and
፨પଣٙΥଣ׌ᅺ๟νОܔͭd݊Ңࡁ௰ชጳሳٙ 5. T.Y. Lin and C.J. Liau (2002), Proc. of the PAKDD’2002 Workshop on Current Trends in Computing (RSCTC), pp. 133-142, LNAI 3066.
the Foundation of Data Mining (As a special issue of Communication 4. Y. Yao, C.J. Liau, and N. Zhong (2003), Granular computing based on Research Fellows
ሙᕚf of IICM, Vol. 5, No. 2). rough sets, quotient space theory, and belief functions, Proc. of The
14 International Symposium on Methodologies for Intelligent Systems
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(B) Journals: (ISMIS), pp. 152-159, LNAI 2871.
ఱҦஔᄴࠦϾԊdҢࡁ˴ࠅٙɓ௅΅ʈЪί׵ 1. C.J. Liau (2005), A modal logic framework for multi-agent belief fu- 5. C.J. Liau (2003), Epistemic logics for information fusion, Proc. of the
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sion, ACM Transactions on Computational Logic, 6(1), 124-174. 7 European Conference on Symbolic and Quantitative Approaches to
ʔΝٙʔ๟ܓપଣᜌ፨ʘ׌ሯٙ޼Ӻd྅݊ᅼᇔᜌ 2. C.J. Liau (2004), Belief fusion and revision: An overview based on Reasoning with Uncertainty (ECSQARU), pp. 489-501, LNAI 2711.
epistemic logic semantics, Journal of Applied Non-Classical Logics 6. C.J. Liau (2003), An overview of hybrid possibilistic reasoning, Proc.
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፨d̙ঐᜌ፨dᅵ࿒ᜌ፨dૢ΁ᜌ፨dʿڢఊሜᜌ (Special Issue on Uncertainty, Incompleteness, Imprecision and Con- of the 9 International Conference on Rough Sets, Fuzzy Sets, Data
Mining and Granular Computing (RSFDGrC), pp. 668-675, LNAI
Research Fellows
ਿ ͉ ༟ ࣘ ፨ഃഃf̤̮dҢࡁɰ༊ഹމவԬʔΝٙᜌ፨౤̈ 3. fl ict in Multiple Data Sources), 14(3), 247-274. 2639.
ਿ ͉ ༟ ࣘ
C.J. Liau (2004), Matrix representation of belief states: An algebraic
semantics for belief logics, International Journal of Uncertainty, 7. Y.T. Chiang, Y.C. Chiang, T.-s. Hsu, C.J. Liau, and D.W. Wang (2002),
ɓࡈɓߧ׌ٙݖ࿴f Fuzziness and Knowledge-based Systems, 12(5), 613-633. How much privacy? - A system to safe guard personal privacy while
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ᔖcc၈j޼ӺࡰResearch Fellow (2005--) 4. D.W. Wang, C.J. Liau, and T.-s. Hsu (2004), Medical privacy protec- releasing database, Proc. of the 3 International Conference on Rough
tion based on granular computing, Artificial Intelligence in Medicine, Sets and Current Trends in Computing (RSCTC), pp. 226-233, LNAI

௰৷ኪዝj Ph.D., CSIE, National Taiwan University ڐԸdҢࡁɰഹࠠ׵౽ᅆۨпଣழ᜗ᜌ፨׌ሯ 32(2), 137-149. 2475.
5. C.J. Liau (2003), Belief, information acquisition, and trust in multi 8. T.-s. Hsu, C.J. Liau, D.W. Wang, and J.K.-P. Chen (2002), Quantifying
(1992) ʘઞীfܼ̍əٝ࿒પଣd༸ᅃપଣၾ਋λપଣഃ agent systems — A modal logic formulation, Artifi cial Intelligence, privacy leakage through answering database queries, Proc. of the 5 th
149(1), 31-60. International Conference on Information Security (ISC), pp. 162-175,
ཥcc༑j+886-2-2788-3799 ext. 1713 ʔΝٙᜌ፨ӻ୕dԨ˸Ϥމਿᓾীሞ౽ᅆۨпଣழ 6. Y.C. Chiang, T.-s. Hsu, S. Kuo, C.J. Liau, and D.W. Wang (2003), LNCS 2433.
Preserving confidentiality when sharing medical database with the 9. G.S. Huang, X. Jia, C.J. Liau, and J.H. You (2002), Two-literal logic

ෂccॆj+886-2-2782-4814 ᜗ٙɓԬत׌f Cellsecu system, International Journal of Medical Informatics, 71, programs and satisfiability representation of stable models: A compari-
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17-23. son, Proc. of the 15 Canadian Conference on Artifi cial Intelligence
ཥɿڦᇌjliaucj@iis.sinica.edu.tw 7. T.F. Fan, C.J. Liau, and Y. Yao (2002), On modal and fuzzy decision 10. (AI), pp. 119-131, LNAI 2338.
T.S. Hsu, C.J. Liau, and D.W. Wang (2001), A logical model for pri-
logics based on rough set theory, Fundamenta Informaticae, 52(4),
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323-344. vacy protection, Proc. of the 4 International Conference on Informa-
ၣccࠫjhttp://www.iis.sinica.edu.tw/pages/liaucj 8. C.J. Liau and D.R. Liu (2001), A possibilistic decision logic with ap- tion Security (ISC), pp. 110-124, LNCS 2200.
Research Description
Research Description plications, Fundamenta Informaticae, 46(3), 199-217. 11. C.J. Liau and Y.Y. Yao (2001), Information retrieval by possibilistic
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9. C.J. Liau (2001), A logical analysis of the relationship between com- reasoning, Proc. of the 12 International Conference on Database and
mitment and obligation, Journal of Logic, Language, and Information, Expert Systems Applications (DEXA), pp. 52-61, LNCS 2113.
• Associate Research Fellow, Institute of Information My research interest is on the investigation of the 10(2), 237-261. 12. C.J. Liau (1999), Many-valued dynamic logics for qualitative decision
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theory, Proc. of the 7 International Workshop on Rough Sets, Fuzzy
essence of rationality and knowledge and its applica- 10. C. J. Liau (2000), An overview of rough set semantics for modal and
Science, Academia Sinica(1997-2005), quantifi er logics, International Journal of Uncertainty, Fuzziness and Sets, Data Mining, and Granular-Soft Computing (RSFDGrC), pp.
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tion to artificial intelligence. We mainly investigate Knowledge-based Systems, 8(1), 93-118. 294-303, LNAI 1711. (An abstract also appears in the 11 Internation-
• Assistant Research Fellow, Institute of Information how a rational agent acquires knowledge, represents the 11. C. J. Liau (1999), On the possibility theory-based semantics for logics al Congress on Logic, Methodology and Philosophy of Science (LMPS),
of preference, International Journal of Approximate Reasoning, 20(2), p. 124).
Science, Academia Sinica(1992-1997), knowledge by logic and acts and makes decisions based 173-190. 13. C.J. Liau and D.R. Liu (1999), A logical approach to fuzzy data analy-
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on logical reasoning. In particular, our most interested 12. C.J. Liau (1998), Possibilistic residuated implication logics with sis, Proc. of the 3 European Conference on Principles and Practice
• Ph.D., CSIE, National Taiwan University (1992), applications, International Journal of Uncertainty, Fuzziness and of Knowledge Discovery in Databases (PKDD), pp. 412-417, LNAI
topic is the rationality criteria of logical reasoning Knowledge-based Systems, 6(4), 365-385. 1704.
• M.S., CSIE, National Taiwan University (1987), under the environment of incomplete and insufficient 13. C.J. Liau (1997), Representing defaults in the framework of possibility 14. C.J. Liau (1998), Modal reasoning and rough set theory, Proc. of the
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theory, International Journal of General Systems, 25(4), 373-387. 7 International Conference on Artifi cial Intelligence: Methodology,
information. Systems, and Applications (AIMSA), pp. 317-330, LNAI 1480.
• B.S., CSIE, National Taiwan University (1985), 14. T.F. Fan, I.P. Lin and C.J. Liau (1997), Nonmonotonic reasoning based
on incomplete logic, Journal of Applied Non-Classical Logics, 7(4), 15. C.J. Liau and I.P. Lin (1994), Gentzen sequent calculus for possibilis-
Technically, a major part of our work involves the 375-395. tic reasoning, In M. Masuch and L. Polos (Eds.) Knowledge Represen-
study of properties of different types of logic for uncer- 15. C.J. Liau (1996), An algebraic formalization of the relationship be- tation and Reasoning under Uncertainty, pp.31-40, LNAI 808.
tween evidential structures and data tables, Fundamenta Informaticae, 16. C.J. Liau and I.P. Lin (1993), Reasoning about higher order uncer-
tain reasoning, for example, fuzzy logic, possibilistic 27(1), 57-76 tainty in possibilistic logic, Proc. of The 7th International Symposium
logic, modal logic, conditional logic, nonmonotonic 16. C.J. Liau and I.P. Lin (1996), Possibilistic reasoning — A mini-survey on Methodologies for Intelligent Systems (ISMIS), pp.316-325, LNAI
and uniform semantics. Artifi cial Intelligence, 88(1-2), 163-193. 689.
logic, etc. Furthermore, we also develop some uniform 17. C.J. Liau and I.P. Lin (1995), A theoretical investigation into quantita-
frameworks for these different types of logic. tive modal logic, Fuzzy Sets and Systems, 75(3), 355-363. (D) Book Chapter:
18. C.J. Liau and I.P. Lin (1993), Proof methods for reasoning about pos- 1. T.F. Fan, D.R. Liu, and C.J. Liau (2005), Justification and hypothesis

sibility and necessity, International Journal of Approximate Reason- selection in data mining, T. Y. Lin, S. Ohsuga, C. J. Liau, X. Hu, and S.
Recently, we have also concentrated on the for-
ing, 9(4), 327-364. Tsumoto (eds.), Foundations of Data Mining and Knowledge Discov-
malization of logical properties of intelligent agents. 19. C.J. Liau and I.P. Lin (1992), Abstract minimality and circumscription, ery, pp.119-130, Springer-Verlag, 2005
Based on different logical systems like epistemic rea- Artifi cial Intelligence, 54, 381-396. 2. T.Y. Lin and C.J. Liau (2005), Granular computing and rough sets:
20. C.J. Liau and I.P. Lin (1991), Fuzzy term rewriting system, Fuzzy Sets An incremental approach, O. Maimon and L. Rokach (eds.), The Data
soning, deontic reasoning, preferential reasoning, etc., and Systems, 44, 1-15. Mining and Knowledge Discovery Handbook, pp.535-561, Springer-
we can discuss some characteristic features of intelli- 21. C.J. Liau and I.P. Lin (1988), Fuzzy logic with equality, International Verlag, 2005
Journal of Pattern Recognition and Artificial Intelligence, 2(2),
gent agents. 351-365.
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