-- T owards conversational agents that are capable of handling more complex questions on contractual conditions, formalizing contract statements in a machine readable way is crucial. However, constructing a formal model which captures the full scope of a contract proves difficult due to the overall complexity its set of rules represent. Instead, this paper presents a top-down approach to the problem. A user-friendly tool we developed for this purpose allows to do so easily and at scale. Then, we expose the statements as service so they can get smoothly integrated in any chatbot framework. For a long time, researchers in artificial intelligence (AI) have been intrigued by the idea of developing a conversational agent that is capable of having a coherent conversation with humans [1]-[3]. Recent breakthroughs in semantics and speech recognition have given rise to hopes for robust solutions to the problem [4], [5]. Major information technology companies have released digital assistants and chatbot frameworks to facilitate the building of conversational agents [6], [7].

This article is derived from the 2005 Innovative Applications of Artificial Intelligence conference invited talk "AI Meets Web 2.0: Building the Web of Tomorrow, Today" presented by Jay M. Tenenbaum in Pittsburgh, Pennsylvania. I invited Marty to deliver that talk at IAAI-05 because he is a true AI and web visionary. It is common in times of grade inflation and hyperbole that speakers and authors are introduced as "visionaries." Jay M. Tenenbaum (or Marty, as he is known) actually is a world-renowned Internet commerce pioneer and visionary. Mary envisioned the commercial and industrial use of the Internet more than a decade before it became a reality, at a time when only academics, industrial R&D groups, and government labs had access to it.

This book proposes to separate knowledge from software and to make it a commodity that is called knowware. The architecture, representation and function of Knowware are discussed. The principles of knowware engineering and its three life cycle models: furnace model, crystallization model and spiral model are proposed and analyzed. Techniques of software/knowware co-engineering are introduced. A software component whose knowledge is replaced by knowware is called mixware. An object and component oriented development schema of mixware is introduced. In particular, the tower model and ladder model for mixware development are proposed and discussed. Finally, knowledge service and knowware based Web service are introduced and compared with Web service. In summary, knowware, software and hardware should be considered as three equally important underpinnings of IT industry. Ruqian Lu is a professor of computer science of the Institute of Mathematics, Academy of Mathematics and System Sciences. He is a fellow of Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering and knowledge based software engineering. He has published more than 100 papers and 10 books. He has won two first class awards from the Academia Sinica and a National second class prize from the Ministry of Science and Technology. He has also won the sixth Hua Loo-keng Mathematics Prize.

Imagine an Internet-scale knowledge system where people and intelligent agents can collaborate on solving complex problems in business, engineering, science, medicine, and other endeavors. Its resources include semantically tagged websites, wikis, and blogs, as well as social networks, vertical search engines, and a vast array of web services from business processes to AI planners and domain models. Research prototypes of decentralized knowledge systems have been demonstrated for years, but now, thanks to the web and Moore's law, they appear ready for prime time. This article introduces the architectural concepts for incrementally growing an Internet-scale knowledge system and illustrates them with scenarios drawn from e-commerce, e-science, and e-life.