Learning Concepts through Multi-Class Diverse Density

This research investigates the possibility of creating an intelligent system based on the philosophy that the world is ambiguous and a system gains knowledge by learning from these ambiguous examples where the learning can especially be improved when a system is allowed to play an active role in requesting these ambiguous examples. The above philosophy will bridge the gap between the traditional Artificial Intelligence (knowledgebased AI) and the behaviour-oriented Artificial Intelligence (intelligence emerging from behaviour). Concept learning, due to its simplicity and features needed to prove this philosophy, is chosen as the studied platform. Based on the aforementioned philosophy, the task of concept learning is comparable to the multiple-instance learning framework where the learning framework will be modified to tackle more classes compared the the original two-class problem, named here as the multi-class problem. The multi-class multipleinstance learning problem is thus defined. One of the methods used to solve the original multiple-instance learning framework, the Diverse Density method, is selected due to its simplicity, robustness, and incremental property. The method is then modified to solve the newly defined multi-class multiple-instance learning problem. To explore the functionality and the efficiency, the modified method, multi-class Diverse Density, was tested on both artificial data and real-world applications: stock prediction task, assembly task, and document search. It was found that redefining the two-class problem as multi-class problems allows a wider range of ambiguous concepts to be better captured than is possible with the original multiple-instance learning framework. Moreover interactivity, the ability to play an active role in requesting or suggesting examples to learn, was proven to enhance the learning process when integrated into the multi-class Diverse Density method. In summary this research proves that the task of concept learning of ambiguous objects can be solved using the proposed multi-class Diverse Density method where the added interactivity feature improves the learning further