Integrating knowledge capture and supervised learning through a human-computer interface

Some supervised-learning algorithms can make effective use of domain knowledge in addition to the input-output pairs commonly used in machine learning. However, formulating this additional information often requires an indepth understanding of the specific knowledge representation used by a given learning algorithm. The requirement to use a formal knowledge-representation language means that most domain experts will not be able to articulate their expertise, even when a learning algorithm is capable of exploiting such valuable information. We investigate a method to ease this knowledge acquisition through the use of a graphical, human-computer interface. Our interface allows users to easily provide advice about specific examples, rather than requiring them to provide general rules; we leave the task of properly generalizing such advice to the learning algorithms. We demonstrate the effectiveness of our approach using the Wargus real-time strategy game, comparing learning with no advic...

• Type: Journal
• Year: 2011
• Where: KCAP
• Authors: Trevor Walker, Gautam Kunapuli, Noah Larsen, David Page, Jude W. Shavlik
• Keywords: Formal Knowledge Representation, Human Computer Interface, Information Technology, KCAP 2011, Knowledge Representation Language