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The National Information Infrastructure (NII) will have profound effects on the lives of every citizen. It promises to deliver to people in their homes and offices a vast array of information in many forms, changing the ways in which business is conducted, offering new educational opportunities, bringing geographically dispersed library resources and entertainment materials to everyone's doorstep. It will connect people to people, and help them with their jobs and tasks. For the NII to be useful, however, people will need easy and efficient access to its resources. Today's computers are complex and difficult to use, even for experts. The NII will be orders of magnitude more complex than current systems; it could easily become a labyrinth of databases and services that is inconvenient for experts and inaccessible to many Americans. The field of artificial intelligence (AI) can play a pivotal role in meeting major challenges of the NII. AI uses the theoretical and experimental tools of ...

Clearly, the second approach is more cautious. Intuitively, it demands that there is a specific argument for τ that is contained in each rational stance a reasoner can take given Γ, DRules, and SRules. The first option doesn't bind the acceptability of τ to a specific argument: it is sufficient if according to each rational stance there is some argument for τ. In Default Logic, the main representational tool is that of a default rule, or simply a default.

The beginning of logic programming can be attributed to Kowalski and Colmerauer. Kowalski formulated the procedural interpretation of Horn clause logic and showed that the axiom A if B can be read as a procedure of a recursive programming language, where A is the procedure head and B is its body. At the same time, early 1970's, Colmerauer and his group at the University of Marseille-Aix developed a specialized theorem prover, which they used to implement natural processing systems. The theorem prover, they called it PROLOG (for Programation et Logique or Programming in Logic), embodied Kowalski's procedural interpretation.

To make ethics computable, we've adopted an approach to ethics that involves considering multiple prima facie duties in deciding how one should act in an ethical dilemma. We believe this approach is more likely to capture the complexities of ethical decision making than a single, absolute-duty ethical theory. However, it requires a decision procedure for determining the ethically correct action when the duties give conflicting advice. To solve this problem, we employ inductive-logic programming to enable a machine to abstract information from ethical experts' intuitions about particular ethical dilemmas, to create a decision principle. We've tested our method in the MedEthEx proof-of-concept system, using a type of ethical dilemma that involves 18 possible combinations of three prima facie duties. The system needed just four training cases to create an ethically significant decision principle that covered the remaining cases.

The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. Because one of the core competencies of Siemens is to provide such highly engineered and customized systems, ranging from solutions for medium-sized and small businesses up to huge industrial plants, the efficient implementation and maintenance of configurators are important goals for the success of many departments. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period. In particular, we highlight the main technology factors regarding knowledge representation, reasoning, and integration which were important for our achievement. Finally we describe selected key application areas where the business success vitally depends on the high productivity of configuration processes.

Machine learning is to learn from data repetitively and to find the pattern hidden there. By applying the results of learning to new data, in other word Machine learning allows computers to analyze past data and predict future data. Machine learning is widely used in familiar places such as product recommendation system and face detection of photos. Also, as cloud machine learning services such as Microsoft's "Azure Machine Learning", Amazon's "Amazon Machine Learning", and Google's "Cloud Machine Learning" are released. This article is written to help novices and experts alike find the best Machine learning books to start with or continue their education. So here is a list of the best Machine learning Books: Book Name: Machine Learning This textbook provides a single source introduction to the primary approaches to machine learning Good content explained in very simple language. The book covers the concepts and techniques from the various fields in a unified fashion and very recent subjects such as genetic algorithms, re-enforcement learning and inductive logic programming. Writing style is clear, explanatory and precise.

I had a fun ride attending a very interesting lecture this semester called Programming Paradigms. I learned about the four main paradigms that exist: imperative, object-oriented, functional and logic programming. Now, I'm sure every developer has heard about imperative, OO and functional, but to be honest I had no idea what logic programming was about. I was intrigued, what could this paradigm I had never heard about be, what does it excel in and could it be useful for day-to-day programming problems? The book The Pragmatic Programmer has a tip called "Invest Regularly in Your Knowledge Portfolio": Different languages solve the same problems in different ways.

We propose a family of probabilistic description logics (DLs) that are derived in a principled way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two-dimensional semantics similar to temporal DLs and are well-suited for representing subjective probabilities. We carry out a detailed study of reasoning in the new family of logics, concentrating on probabilistic extensions of the DLs ALC and EL, and showing that the complexity ranges from PTime via ExpTime and 2ExpTime to undecidable.

Whenever one separates sociology and technology, one misses the point.