Intelligent Backtracking in the Echidna Constraint Logic Programming System
نویسنده
چکیده
Abstract: A new type of reasoning engine for model-based expert system applications is described which employs recent advances in constraint logic programming (CLP), intelligent backtracking (IB) and schema (object-oriented) knowledge representations. Constraint reasoning and logic programming (LP) have been recently joined in the new field of CLP. There is keen research interest and new CLP languages are emerging. IB techniques using reason maintenance systems (RMS) have also been extensively explored and adapted to LP. As well, object-oriented knowledge structures have been incorporated into the LP paradigm. All three of these capabilities are important for model-based reasoning systems. We have developed a new CLP language, called Echidna1, which supports model-based reasoning using schema knowledge structures; a clausal reasoner based on SLD-resolution; constraint solving for discrete, integer and real variables using a generalized form of arc-consistency; and complete IB using a hybrid RMS. In this paper, we describe our method for realizing IB within the Echidna language. A new type of reasoning engine for model-based expert system applications is described which employs recent advances in constraint logic programming (CLP), intelligent backtracking (IB) and schema (object-oriented) knowledge representations. Constraint reasoning and logic programming (LP) have been recently joined in the new field of CLP. There is keen research interest and new CLP languages are emerging. IB techniques using reason maintenance systems (RMS) have also been extensively explored and adapted to LP. As well, object-oriented knowledge structures have been incorporated into the LP paradigm. All three of these capabilities are important for model-based reasoning systems. We have developed a new CLP language, called Echidna1, which supports model-based reasoning using schema knowledge structures; a clausal reasoner based on SLD-resolution; constraint solving for discrete, integer and real variables using a generalized form of arc-consistency; and complete IB using a hybrid RMS. In this paper, we describe our method for realizing IB within the Echidna language.
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