The Object-oriented Architecture of the Syntactic Pattern-recognition System Based on GDPLL(k) Grammars

• Autorzy: Jurek J.
• Języki publikacji: EN

Abstrakty

EN

The syntactic pattern recognition model based on GDPLL (k) grammars has been proposed [6, 13] as an efficient tool for inference support in diagnostic and control expert systems. In this paper we discuss the software engineering aspect of the syntactic pattern recognition (sub)system. The architecture of the system should allow to embed the system in real-time environments, accumulate knowledge about the environment, and flexible react to the changes in the environment. The object-oriented approach has been applied to design the system, and the Unified Modeling Language has been used for the specification of the software model. In the paper we presented the model and its practical applications.

Słowa kluczowe

EN

syntactic pattern recognition; context sensitive languages; parsing; grammatical inference; object-oriented approach; software engineering

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2004
• Tom: Vol. 13
• Strony: 83--102
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Jurek J.

• Institute of Computer Science, Jagiellonian University, Nawojki 11, 30-072 Cracow, Poland,

Bibliografia

• [1]Aho A.V., Ullman J.D.; The Theory of Parsing, Translation, and Compiling, Prentice-Hall, Englewood Cliffs, NJ, 1972.
• [2]Alquezar R., Sanfeliu A.; Recognition and learning of a class of context-sensitive languages described by augmented regular expressions, Pattern Recognition, 30,1, 1997, pp. 163-182.
• [3]Booch G.; Object-Oriented Analysis and Design with Applications, Second Edi¬tion, The Benjamin/Cummings Publishing Company, 1994.
• [5]Booch G., Rumbaugh J., Jacobson I.; The Unified Modeling Language Reference Manual, Addison-Wesley, 1999.
• [6]Flasiński M., Jurek J.; Dynamically Programmed Automata for Quasi Context Sensitive Languages as a Tool for Inference Support in Pattern Recognition- Based Real-Time Control Expert Systems, Pattern Recognition, 32,4, 1999, pp. 671-690.
• [7]Flasiński M., Reroń E., Jurek J., Wójtowicz P., Atlasiewicz K.; Mathematical linguistics model for medical diagnostics of organ of hearing in neonates, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), 3019, 1994.
• [8]Fu K.S.; Syntactic Pattern Recognition and Applications, Prentice Hall, Englewood Cliffs, 1982.
• [9] Higuera De La C.; Current Trends in Grammatical Inference. Lecture Notes in Computer Science, 1876, 2000, pp. 28-31.
• [10]Jurek J.; On the Linear Computational Complexity of the Parser for Quasi Context Sensitive Languages. Pattern Recognition Letters, 21, 2000, pp. 179— 187.
• [11]Jurek J.; Syntactic Pattern Recognition-Based Agents for Real-Time Expert Systems, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), 2296, 2002, pp. 161-168.
• [12]Jurek J., On the construction of hybrid expert systems for the application in the power industry, (Paper in Polish) Proc. 5th National Conf. "Inżynieria Wiedzy i Systemy Ekspertowe”, Wroclaw, Poland, June 11-13, 2003, 2, 2003, pp. 54-61.
• [13]Jurek J.; The Generalised Model of DPLL(k) Automata for Applications in Real-Time Expert. Systems, Proc. 3rd Conf. On Computer Recognition Systems, KOSYR’03, Miłków, Poland, May 26-29, 2003, 2003, pp. 321-326.
• [14]Jurek J.; Towards grammatical inferencing of GDPLL(k) grammars for applications in syntactic pattern recognition-based expert systems, submitted for ICAISA 2004: 7th International Conf. On Artificial Intelligence And Soft Computing, Zakopane, Poland, June 7-11, 2004.
• [15]Piętka E.; Feature extraction in computerized approach to the ECG analysis, Pattern Recognition, 24,2, 1991, pp. 139-146.
• [16]Rosenkrantz D.J., Stearns R.E.; Properties of deterministic top-down grammars, Information and Control, 17, 1970, pp. 226-256.
• [17]Sakakibara Y.; Recent Advances of Grammatical Inference, Theoretical Computer Science, 185,1, 1997, pp. 15-45.