Member (2000-2005) of the Editorial Board of AI Communications, The European Journal on Artificial Intelligence, closely associated with ECCAI, the European Coordinating Comittee for Artificial Inteligence. Member of the Program Committee of TTIA'2005, VI Jornadas de Transferencia Tecnológica de Inteligencia Artificial, held in conjuntion with CAEPIA'2005, Conference of the Spanish Association of Artificial Intelligence (AEPIA). Member of the Program Committee of STAIRS'2004 and STAIRS'2002, STarting Artificial Intelligence Researchers Symposium, held in conjunction with ECAI, the European Conference on Artificial Intellligence. Member of the Program Committee of the following workshops: IKOMAT'2002 (First International Workshop on Intelligent Knowledge Management Techniques, held in conjunction with KES'2002, Sixth International Conference on Knowledge-Based Intelligent Information and Engineering Systems), TARRAT'99 (Workshop on Temporal Reasoning, held in conjunction with CAEPIA'99), Advances in Propositional Deduction 1996 (held in conjunction with ECAI'96, Twelfth European Conference on Artificial Intelligence. Member (2000-2005) of the Editorial Board of AI Communications, The European Journal on Artificial Intelligence, closely associated with ECCAI, the European Coordinating Comittee for Artificial Inteligence.

On a recent visit to the doctor, Edward Feigenbaum had the eerie experience of seeing one of his inventions used in a way he never expected: His 25-year-old concept was being used to diagnose a problem with his own breathing. "It's using artificial intelligence," the doctor patiently explained about the spirometer, which measures airflow. A professor of computer science and co-scientific director of the Knowledge Systems Laboratory at Stanford University, Feigenbaum is a pioneer of artificial intelligence (AI) -- the science of making machines think like humans. Dozens of applications have their roots in the Stanford lab he started in 1965 and in related software programs that solve complex problems the same way human experts do. Feigenbaum was the first person to realize that human intelligence springs not from rules of logic but from knowledge about particular problems (whether it's chemistry or auto mechanics) and about the world in general.

A goal for which a value is sought during an expert system consultation. Primary goals are terminal objectives for the consultation: once their value are found, or a determination is made that they cannot be found, the consultation ends.

Artificial Intelligence (AI) techniques are now being used by the practicing engineer to solve a whole range of hitherto intractable problems. This journal provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering. Engineering Applications of Artificial Intelligence publishes: • Survey papers/tutorials.

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).

Note added 3 Mar 2007: I and some others had some reservations about the document because we felt it did not adequately distinguish degrees that were mainly concerned with training for particular vocations involving use of computers and development of software systems from degrees that were more concerned with teaching the foundations of the subject and preparing future researchers to extend both the foundations and the techniques. This meant, in particular, that some of us felt that there was insufficient emphasis on the mathematical content of computer science and the possibility of doing theoretical (e.g. I personally felt then, and still feel that that expressed a narrowness of vision in the majority of members of the computer science community. The more detailed overview is given in section C, as an indication of the scope of the field. This might be used by reviewers considering degrees in AI, not in order to define what should or should not be in such a degree, but in order to provide some background that might be useful when assessing an AI degree course, or possibly when designing one. There is no implication that everything mentioned here must be included in a degree with "Artificial Intelligence" in its title, or that topics not included here are excluded.

Compared with other medical fields, clinical toxicology is probably easier to formalise because few heuristics are used and lots of data can be managed in a database, such as storing information about drugs and toxicological classes. From the beginning of the analysis, we have intentionally separated data and knowledge. In SETH, data on drugs, toxicological classes and advice can be updated within the data base application. The maintenance of data on drugs and toxicological classes is performed with an electronic dictionary of French drugs available in our University Hospital and is updated every three months (20). Only reasoning and toxicological classes interaction updates have to be done in the knowledge base.

Computers can be programmed with rules to use information [information: data with context or meaning ] to make simple decisions. This is knowledge [knowledge: the ability to understand information and, to then form judgements, opinions, make predictions and decisions based on that understanding ] that has been passed on from the programmer. A simple example of this is a spreadsheet [spreadsheet: A spreadsheet is made up of cells, rows and columns. Each cell holds a piece of numeric (numbers) or alphanumeric (text) data. Cells can also contain formulae to calculate their contents.

EXPERT SYSTEMS Computers as sages by Howard Rheingold Howard Rheingold is the author of Software Odyssey and co-author of Higher Creativity. Should you ever want to drill for oil, diagnose a disease or synthesize a new molecule, you can ask Prospector, MYCIN or Dendral for some sage advice. They are certified experts in their respective fields. They are also computer programs. We all depend on expert assistance-from doctors, attorneys, automobile mechanics, computer repairmen.

A consultation program plays the role of an expert consultant in some domain, giving advice or answers to non-experts with problems In the domain. Users will often want to know how the system arrived at its results during a particular consultation. This paper explains how the implementation of such a program as a production system can facilitate program-generated explanations. A production system [2] consists of three basic components: a set of production rules, a data base which is both used and updated by these rules, and a rule interpreter. A production rule often is in the form of a situation-action rule: it describes a situation and a set of actions to be taken if this situation is found to exist.