Connectionism-inspired knowledge modeling for industrial systems

This paper introduces connectionistic concept grid (CCG), which is used for representation and reasoning of engineering knowledge applied for the industrial systems. The paper also presents the Crossword tool which was used for computer aided modelling and execution of CCG. CCG is able to provide semantic descriptions of objects and processes and was developed to overcome limitations of earlier knowledge approaches in expression of temporal relations between objects. The scope of this article includes an explanation of the principles for the proposed approach. The execution principles of the model, description of the tool and simple illustrative scenario of industrial equipment used in automated assembly line are discussed. It is assumed that the equipment interfaces are implemented as web services.

[1]  N. Lohse,et al.  An ontology for the definition and validation of assembly processes for evolvable assembly systems , 2005, (ISATP 2005). The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, 2005..

[2]  Steven R. Ray,et al.  Manufacturing interoperability , 2003, J. Intell. Manuf..

[3]  Per Flensburg,et al.  Ontologies in practice , 2005 .

[4]  Naomi Goldblum The brain-shaped mind , 2001 .

[5]  A. Lobov,et al.  A connectionistic knowledge-based approach to the modelling and control of manufacturing systems , 2008, 2008 First Conference on IT Revolutions.

[6]  José Barata,et al.  A Multiagent Control System for Shop Floor Assembly , 2007, HoloMAS.

[7]  E. Rosch,et al.  Family resemblances: Studies in the internal structure of categories , 1975, Cognitive Psychology.

[8]  Svetan M. Ratchev,et al.  Towards an Integrated Assembly Process Decomposition and Modular Equipment Configuration - A Knowledge Enhanced Iterative Approach , 2006, IPAS.

[9]  A. Lobov,et al.  Service oriented architecture in developing of loosely-coupled manufacturing systems , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[10]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[11]  Lokendra Shastri,et al.  Connectionist mechanisms for cognitive control , 2005, Neurocomputing.

[12]  Alois Zoitl,et al.  Intelligent reconfiguration using knowledge based agent system , 2005, 2005 IEEE Conference on Emerging Technologies and Factory Automation.

[13]  Xiao Hui Wang,et al.  A knowledge model for functional re-design , 2000 .

[14]  Svetan M. Ratchev,et al.  Knowledge-Based Requirements Engineering for Reconfigurable Precision Assembly Systems , 2004, BASYS.

[15]  Pedro M. Domingos,et al.  Ontology Matching: A Machine Learning Approach , 2004, Handbook on Ontologies.

[16]  Lokendra Shastri,et al.  Advances in SHRUTI—A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony , 1999, Applied Intelligence.

[17]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[18]  Hector J. Levesque,et al.  Knowledge Representation and Reasoning , 2004 .

[19]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[20]  R. W. Lewis,et al.  Programming Industrial Control Systems Using IEC 1131-3 , 1995 .

[21]  Svetan Ratchev,et al.  Equipment ontology for modular reconfigurable assembly systems , 2005 .

[22]  Barbara Hannan,et al.  Connectionism and the Mind: An Introduction to Parallel Processing in Networks , 1992 .

[23]  Pedro M. Domingos,et al.  iMAP: discovering complex semantic matches between database schemas , 2004, SIGMOD '04.