Description logics are embodied in several knowledge-based systems and are used to develop various real-life applications. Now in paperback, The Description Logic Handbook provides a thorough account of the subject, covering all aspects of research in this field, namely: theory, implementation, and applications. Its appeal will be broad, ranging from more theoretically oriented readers, to those with more practically oriented interests who need a sound and modern understanding of knowledge representation systems based on description logics. As well as general revision throughout the book, this new edition presents a new chapter on ontology languages for the semantic web, an area of great importance for the future development of the web. In sum, the book will serve as a unique resource for the subject, and can also be used for self-study or as a reference for knowledge representation and artificial intelligence courses. "On the whole, this book is a valuable resource for any scientist or institution working in areas related to applications implementation.

For the purposes of this list, only the first category is interesting, because these are true AI agents. Agents in this category have sensing and reasoning abilities that can be quite different than humans and have to be based on some sort of artificial knowledge structure and reasoning process. What I find most interesting is to examine how this mechanism is defined and what its capabilities are. The sensing and reasoning mechanisms are effectively identical to humans. This allows the film to treat the behavior as if it were identical to humans (or nearly so, e.g.

The rapidly developing field of diagrammatic knowledge representation and reasoning is surveyed. The origins and rationale of the field, basic principles and methodologies, as well as selected applications are discussed. Closely related areas, like visual languages, data presentation, and visualization are briefly introduced as well. Basic sources of material for further study are indicated.

A legal knowledge based system called JUSTICE is presented which can identify heterogeneous representations of concepts across all major Australian jurisdictions, and some concepts within US and UK cases. The knowledge representation scheme used for legal and common sense concepts is inspired by human processes for the identification of concepts and the expected order and location of concepts. These are supported by flexible search functions and various string utilities. JUSTICE is a client-based legal software agent which works with both plaintext and HTML representations of legal cases over file systems, and the World Wide Web. In creating JUSTICE an ontology for legal cases was developed, and this is implicit within JUSTICE.

There is a big difference between the attention artificial intelligence (AI) is currently receiving and that of the 1990s. Twenty years ago, the focus was on logic-based AI, usually under the heading of knowledge representation, or KR, whereas today's focus is on machine learning and statistical algorithms. This shift has served AI well, since machine learning and stats provide effective algorithmic solutions to certain kinds of problems (such as image recognition), in a way that KR never did. However, I contend the pendulum has swung too far, and something valuable has been lost. Knowledge representation is not a single thing.

Many and long were the conversations between Lord Byron and Shelley to which I was a devout and silent listener. During one of these, various philosophical doctrines were discussed, and among others the nature of the principle of life, and whether there was any probability of its ever being discovered and communicated. They talked of the experiments of Dr. Darwin (I speak not of what the doctor really did or said that he did, but, as more to my purpose, of what was then spoken of as having been done by him), who preserved a piece of vermicelli in a glass case till by some extraordinary means it began to move with a voluntary motion. Not thus, after all, would life be given. Perhaps a corpse would be reanimated; galvanism had given token of such things: perhaps the component parts of a creature might be manufactured, brought together, and endued with vital warmth (Butler 1998).

In 1976, Newell and Simon [Newell and Simon1976] proposed that intelligent behavior arises from the manipulation of symbols--entities that represent other entities, and that the process by which intelligence arises is heuristic search. Search is a process of formulating and examining alternatives. It starts with an initial state, a set of candidate actions, and criteria for identifying the goal state. It is often guided by heuristics, or rules of thumb,'' which are generally useful, but not guaranteed to make the best choices. Starting from the initial state, the search process selects actions to transform that state into new states, which themselves are transformed into more new states, until a goal state is generated.

You may skip most of these readings if you have prior programming experience in Java.

The following is a special contribution to this blog from Henry Kautz, Chair of the Department of Computer Science at the University of Rochester. His research interests are in knowledge representation, satisfiability testing, pervasive computing, and assistive technology. He is currently President of the Association for the Advancement of Artificial Intelligence (AAAI). If you have comments on this essay, e-mail Henry or add an entry to the bottom of this blog post. Countless gallons of ink (real and virtual) have been spilled on the need to infuse the humanities into science and engineering education.

Gister supports the rapid development of evidential reasoning systems through an interactive, menu-driven, graphical interface, based upon Grasper-CL. The user interacts with the system in much the same way as with electronic spreadsheets, by simply selecting from menus to add evidential operations to an analysis, to modify data or operation parameters, or to change any portion of the uncertain knowledge base. In response, gister updates its analyses to reflect the new information. Gister supports a wide range of evidential operations, including fusion, source discounting, time projection, summarization, evidence interpretation, and sensitivity analysis. Gister has been applied to a wide range of problems, including multisensor interpretation, mission planning, medical diagnosis, intelligence analysis, underwater vehicle tracking, antiair threat identification, robot vehicle navigation, and management decision support.