Implementing Telos Bryan M. Kramer, Vinay K. Chaudhri, Manolis Koubarakis, Thodoros Topaloglou, Huaiqing Wang, John Mylopoulos D e p a r t m e n t o f C o m p u t e r Science, U n i v e r s i t y o f Toronto, Toronto, Ontario, Canada. M 5 S 1A4. k r a m e r @ ai.toronto.edu 1.0 Introduction This paper describes experiences in implementing Telos [MBJK90, TK89], a knowledge representation scheme developed at the University of Toronto. In a nutshell, Telos is a hybrid of an object-centered representation for representing entities, a logic-based language for representing deductive rules and integrity constraints, and an interval-based language for representing time. The language is unique for its tight integration of temporal knowledge and for its meta-level capabilities which include a class/metaclass hierarchy and a reflective inference engine. The main motivation for implementing Telos is to discover whether the chosen combination of representations results in an effective and efficient knowledge representation system. Another important goal is to discover how large a knowledge base can be supported using "'traditional" AI implementation methodologies such as the use of LISP and memory resident data-structures. A long
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Implementing Telos
Kramer, Bryan M.; Chaudhri, Vinay K.; Koubarakis, Manolis; Topaloglou, Thodoros; Wang, Huaiqing; Mylopoulos, John
Follow ACM SIGART Bulletin
, Volume 2 (3)
Association for Computing Machinery – Jun 1, 1991
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