In some examples, the adaptive learning system of FIG. 4 may include or be replaced by a fuzzy rule-based system where rules can be extracted or determined by experts and then quantified using fuzzy logic. The fuzzy rule-based system includes a rule base including rules for decision-making, including IF-THEN rules with certain predetermined conditions and boundaries based on expert rule setting. The fuzzy rule-based system also includes a database of membership functions, including the terms used in the fuzzy rules and linguistic variable of the system are defined. An inference system performs a decision-making operation to device conclusions using the given rules and facts or contextual information. The fuzzy rule-based system may include elements or variables to account for trustworthiness of the various IF-THEN rules and adapts the rules based on recursive and iterative learning to develop rules with higher levels of trustworthiness.

In operation, the fuzzy rule-based system receives input data such as the contextual data and other data described above. The data is fuzzified to obtain membership degrees to each of the terms of the fuzzy variables. The inference system applies the rules, using the knowledge base, to output variables which are defuzzified and output as results, in this case describing the rearrangement or location change (if any) for particular elements on the display.