Building Intelligent Agents is unique in its comprehensive coverage of the subject. The first part of the book presents an original theory for building intelligent agents and a methodology and tool that implement the theory. The second part of the book presents complex and detailed case studies of building different types of agents: an educational assessment agent, a statistical analysis assessment and support agent, an engineering design assistant, and a virtual military commander. Also featured in this book is Disciple, a toolkit for building interactive agents which function in much the same way as a human apprentice. Disciple-based agents can reason both with incomplete information, but also with information that is potentially incorrect. This approach, in which the agent learns its behavior from its teacher, integrates many machine learning and knowledge acquisition techniques, taking advantage of their complementary strengths to compensate for each others weakness. As a consequence, it significantly reduces (or even eliminates) the involvement of a knowledge engineer in the process of building an intelligent agent.Table 6.4 The method for generating Which-relevant test questions ac Generate an If-relevant test IF\ with a relevant source S\ ... $3 , starting from T\ : IF3 = (IFQ3 FR3 FW3 H3 EX3) ac Build the Which-relevant test question WRQ corresponding to T\ , S\ ... Further verification, validation and maintenance of the assessment agent was conducted in much the same manner as in traditional software engineering.
|Title||:||Building Intelligent Agents|
|Publisher||:||Morgan Kaufmann - 1998-01-01|