Design and validation of a metamodel for metacognition support in artificial intelligent systems

Computational metacognition is a technical area of artificial intelligence whose aim is to increase the degree of autonomy and awareness an intelligent system has about its own reasoning and learning. In the literature, different models of metacognition are applied to artificial intelligent systems. However many of these models have a narrow focus, because they do not address comprehensively the elements of metacognition. This paper presents an analysis of metacognitive models discussed in the literature in order to discover the common (invariants) and varying (variants) elements. The main contribution of this work is the development of a comprehensive and general purpose metamodel named MISM that covers and describes a broad range of commonly referenced concepts in metacognitive models in the area of artificial intelligence. A validation process was conducted to ensure the reliability of MISM in terms of generality, expressiveness and completeness. The validation was performed using three techniques for improvements and adjustments to the metamodel: (i) comparison with other models; (ii) frequency-based selection; and (iii) model tracing. The adjusted and improved version of the metamodel was named MISM 1.1.