Q2. What are the main parameters to be tuned in searching for relevant2 patterns over new information systems?
One can see that parameters to be tuned in searching for relevant2 patterns over new information systems are, among others, relational structures over value sets, the language of formulas defining parts, and constraints.
Q3. What is the main advantage of rough set theory in data analysis?
The rough set approach seems to be of fundamental importance in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, image processing, signal analysis, knowledge discovery, decision analysis, and expert systems.
Q4. What is the role of information granulation in human problem-solving?
Information granulation can be viewed as a human way of achieving data compression and it plays a key role in the implementation of the strategy of divide-and-conquer in human problem-solving [98].
Q5. What is the key to the presented approach?
It was observed in [44] that the key to the presented approach is provided by the exact mathematical formulation of the concept of approximative (rough) equality of sets in a given approximation space.
Q6. What is the main reason for the need to use local models in modeling complex phenomena?
One should take into account that modeling complex phenomena entails the use of local models (captured by local agents, if one would like to use the multi-agent terminology [25,99]) that next should be fused.
Q7. What is the way to search for the optimal approximation space?
By tuning such parameters according to chosen criteria (e.g., minimal description length) one can search for the optimal approximation space for concept description (see, e.g., [4]).
Q8. What is the definition of the ontology approximation problem?
The ontology approximation problem is one of the fundamental problems related to approximate reasoning in distributed environments.
Q9. What are some of the problems that are difficult to solve using the existing methodologies and technologies?
Among such problems are, e.g., classification of medical images, control of autonomous systems like unmanned aerial vehicles or robots, problems related to monitoring or rescue tasks in multi-agent systems.