The model of the world (W•) is used to answer questions about W**. In order**
to answer questions about the modeller himself, we introduce a model of the modeller (M•). M•**is the self (reflective) knowledge of the agent in the world.**
Before explaining the crucial layers of introspective knowledge for **complex AI** systems, we should explain our idea of how to map and implement the first four layers. The world W **is the application we have in mind, with the capability**
to adapt to new environmental conditions. Thereby, the processing system is the modeller M. Accordingly, W•**is the processing system's knowledge basis,** the ontology terminology box of the AI system's application domain. M•**is the**
internal state of the dialogue system which we implement as information state, consisting of assertion box instances, according to the ontology. **It represents** the self knowledge of the system in the running state. If the information state contains information about the system itself, the modeller's self knowledge can be called reflective.

W•• **is the model of the world knowledge; it contains the meta knowledge**
in order to reason about the questions concerning the world knowledge. Some typical questions for ontological knowledge bases are whether the classes and relations adequately describe the application domain, and whether the **descriptive**
representation of domain processes provides a motivated conclusive representation of the situation in terms of content-describing features**. We implement this**
meta knowledge layer with machine learning models: if the models created by the attributes derived from ontology instances have positive evaluation characteristics (for example, high cross-validated classification accuracy or reasonable symbolic association rules or decision trees), we adequately describe the world knowledge by meta knowledge. (A task-based evaluation of ontologies in a specific application domain is meta knowledge, too.) M•• **is the knowledge that can**
be extracted from the processing system while running the system **in the current environment. This self-reflective knowledge can be used to adapt to other**
processing strategies, for example control signals, if the current one fails. At this layer, we are able to recognise how all other knowledge layers work together to performing a particular task in the AI system's application domain.

Both W•• and M•• can be used to build **decision-oriented operationalised**
management rules**. Decision-oriented means that any of the reaction duties are** directly triggered or effected. Operationalised rules means that the control rules derived from the machine learning models are in a directly executable format (e.g., association rules) or can be translated into these. The operationalisation itself can be undertaken manually or automatically. To sum up the intention of the meta knowledge taxonomy for metacognition. The modelling by a knowledge taxonomy provides abstract solution for the problem of how
- **to monitor system performance;** - **to adapt a problem solving strategy according to performance classification;**
- to build operational machine learning models.