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Metamemory refers to the processes involved in self-regulation or self-awareness of memory. In this paper we describe a novel rule-based architecture of metamemory named M2-Acch. M2-Acch consists of a cycle of reasoning about events that occur in long-term memory (LTM) in an intelligent tutoring system. M2-Acch is composed of a three layer structure: static layer, functional layer and information layer. The structural components of each layer model are described using formal definitions. M2-Acch uses confidence judgments for recommending search strategies for adaptation to changes in the information retrieval constraints. An intelligent tutoring system named FUNPRO was implemented and validated. The results of the experimental tests show that M2-Acch can be used as a valid tool for adapting to changes in the constraints in information retrieval from long-term memory in artificial cognitive systems.

A METAMEMORY MODEL FOR AN INTELLIGENT TUTORING SYSTEM UN MODELO DE METAMEMORIA PARA UN SISTEMA TUTOR INTELIGENTE