Knowledge representation and reasoning

Knowledge Representation is the area of Artificial Intelligence (AI) concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. It is at the very core of a radical idea about how to understand intelligence: instead of trying to understand or build brains from the bottom up, we try to understand or build intelligent behavior from the top down. In particular, we ask what an agent would need to know in order to behave intelligently, and what computational mechanisms could allow this knowledge to be made available to the agent as required. This book is intended as a text for an introductory course in this area of research. There are many different ways to approach and study the area of Knowledge Representation. One might think in terms of a representation language like that of symbolic logic, and concentrate on how logic can be applied to problems in AI. This has led to courses and research in what is sometimes called “logic-based AI.” In a different vein, it is possible to study Knowledge Representation in terms of the specification and development of large knowledge-based systems. From thi

Loading Data into Description Reasoners

Knowledge-base management systems (KBMS) based on description logics are being used in a variety of situations where access is needed to large amounts of data stored in existing relational databases. We present the architecture and algorithms of a system that converts most of the inferences made by the KBMS into a collection of SQL queries, thereby relying on the optimization facilities of existing DBMS to gain e#ciency, while maintaining an object-centered view of the world with a substantive semantics and significantly di#erent reasoning facilities than those provided by Relational DBMS and their deductive extensions. We address a number of optimization issues that arise in the translation process due to the fact that SQL queries with di#erent syntax (but identical semantics) are not treated uniformly by current database management systems

Toward a New Science of Common Sense

Common sense has always been of interest in AI, but has rarely taken center stage. Despite its mention in one of John McCarthy's earliest papers and years of work by dedicated researchers, arguably no AI system with a serious amount of general common sense has ever emerged. Why is that? What's missing? Examples of AI systems' failures of common sense abound, and they point to AI's frequent focus on expertise as the cause. Those attempting to break the brittleness barrier, even in the context of modern deep learning, have tended to invest their energy in large numbers of small bits of commonsense knowledge. But all the commonsense knowledge fragments in the world don't add up to a system that actually demonstrates common sense in a human-like way. We advocate examining common sense from a broader perspective than in the past. Common sense is more complex than it has been taken to be and is worthy of its own scientific exploration