Using Machine Learning Techniques in Search
A typical ability used by people in problem solving is to learn from prior experiences in problem solving and to use the learned knowledge to produce better results or solve harder problems than they would be able to achieve without this learned knowledge. Obviously, especially knowledge-based search should profit very much from the use of machine learning techniques, both to select good suited search approaches and control strategies for the given problem and to guide a system during its search. But there is one important problem that has to be taken into account when developing learning search systems: usually the search approaches employed by computer systems are not the same problem solving approaches that people use and therefore it is not obvious how the learning process of people can be transfered to the search approaches.
When trying to develop a learning search system, we have found the following questions to be a good guideline for the development. The first four questions are concerned with the learning phase of the system:
But learned knowledge can only help a search system if the system can apply this knowledge during its search. This is therefore called the application phase of the system and there are also four questions to guide the development of this part of it:
Finally, at the bottom of all the questions presented so far, there is an elementary question that all learning systems (not only search systems) have to answer:
We have developed various concepts for learning search systems that are based on different answers to several of the questions from above. There are own web-pages for the following approaches:
An overview of all the known answers to the nine questions for the application area automated deduction is given in the following report:
Last Change: 5/12/2013