Cloud Model - A Bidirectional Cognition Model between Concept's Extension and Intension

The expressing and processing of uncertain concepts is a fundamental problem in artificial intelligence. Several theoretical models have been proposed for solving this problem, such as probability theory, fuzzy sets, rough sets, cloud model, et al. Unfortunately, human deals with uncertain concepts based on words (concept intension), while computer based on sample set (concept extension). Many data mining and machine learning methods have been developed for extracting knowledge from data sets in recent years. These methods are unidirectional cognitive computing models from extension to intension. In this paper, a bidirectional cognitive computing model, cloud model, will be introduced. In the cloud model, forward cloud generator and backward cloud generator are designed for the bidirectional transformations between concept’s intension and extension. Some experiment results will be discussed to show the validity and efficiency of cloud model for bidirectional cognitive computing.

[1]  Edward H. Madden.  Problems in the Philosophy of Mind , 1966 .

[2]  Guoyin Wang,et al.  Rough Set Based Uncertain Knowledge Expressing and Processing , 2011, RSFDGrC.

[3]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Guoyin Wang,et al.  A Multi-step Backward Cloud Generator Algorithm , 2012, RSCTC.

[5]  Richard E. Neapolitan,et al.  Contemporary Artificial Intelligence , 2012 .

[6]  Guoyin Wang,et al.  Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.

[7]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[8]  Achim Hoffman.  Paradigms of Artificial Intelligence: A Methodological and Computational Analysis , 1998 .

[9]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[11]  N. Stanietsky,et al.  The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.

[12]  Liu Yu,et al.  Statistics on atomized feature of normal cloud model , 2010 .

[13]  Gregory Piatetsky-Shapiro,et al.  Data Mining and Knowledge Discovery in Business Databases , 1996, ISMIS.

[14]  Nils J. Nilsson,et al.  Artificial Intelligence: A New Synthesis , 1997 .

[15]  Allen Newell,et al.  Computer science as empirical inquiry: symbols and search , 1976, CACM.

[16]  Li De-yi,et al.  A New Algorithm of Backward Cloud , 2004 .

[17]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[18]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[19]  J. Runnenburg.  PROBABILITY THEORY AND ITS APPLICATIONS , 1985 .

[20]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.

[21]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[22]  R. D'Andrade.  The Development of Cognitive Anthropology , 1995 .

[23]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[24]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[25]  E. Bizzi,et al.  The Cognitive Neurosciences , 1996 .

[26]  Deyi Li,et al.  A new cognitive model: Cloud model , 2009 .

[27]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.