

![0_image_0.png](0_image_0.png)

In subsequent applications of the augmented CRISP cycles, the introspective models can be combined with the models of the former CRISP process.

It is important to note that empirical machine learning models are pattern patching systems; we expect the behaviour to be improved by drawing an analogy to a past experience which materialises as patterns to be mined. These patterns do not necessarily follow logical rules in terms of a higher order logic—but instead, they should follow at least the causal implications of a propositional logic which helps to implement reactivity based on learned causality. All patterns to be mined can be regarded as introspective reports **on the application or business**
domain.

## 4 Conclusion

The question we investigated was about the scope and usefulness of a metacognitive model. In order to develop a computational introspective model, empirical machine learning models can be investigated. This should augment cognitive capabilities of adaptable AI systems, especially in the reasoning phase before action taking, which we believe requires to a great extent metacognitive instead of cognitive capabilities.

Similar methodology in computation has received great attention for uncertainty handling, control in decentralised systems, scheduling for planning in real-