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Some Thoughts to Consider 6 What is the difference between Artificial Intelligence and Computer Science? What is the difference between Artificial Intelligence.

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Presentation on theme: "Some Thoughts to Consider 6 What is the difference between Artificial Intelligence and Computer Science? What is the difference between Artificial Intelligence."— Presentation transcript:

1 Some Thoughts to Consider 6 What is the difference between Artificial Intelligence and Computer Science? What is the difference between Artificial Intelligence and Software Engineering? What is the difference between Artificial Intelligence and Object Technology? Why would some people call the ‘Semantic Web’ the ‘Pedantic Web’?

2 Heuristic - An Important Concept The word ‘heuristic is derived from a Greek word meaning ‘to find’ or ‘to discover’. Some people use heuristic as the opposite of algorithmic. Newell and Simon: “A process that may solve a given problem, but offers no guarantees of doing so, is called a heuristic for that problem.” Some people view heuristics as ‘rules of thumb’ that domain experts use to generate good solutions without exhaustive search. This view paved the way for rule-based expert systems. Some generalize heuristic to mean ‘knowledge about a problem solution’. Currently, according to Russell and Norvig, heuristic is most often used as an adjective, referring to any technique that improves the average-case performance on a problem- solving task, but does not necessarily improve the worst-case performance.

3 Main Points of Chapter 8 Knowledge representation languages should be declarative, compositional, expressive, context-independent, and unambiguous. Logics differ in their ontological commitments and epistemological commitments. While propositional logic commits only to the existence of facts, first-order logic commits to the existence of objects and relations and thereby gains expressive power. A possible world, or model, for first-order logic is defined by a set of objects, the relations among them, and the functions that can be applied to them. Constant symbols name objects, predicate symbols name relations, and function symbols name functions. An interpretation specifies a mapping from symbols to the model. Complex terms apply function symbols to terms to name an object. Given an interpretation and a model, the truth of a sentence is determined. An atomic sentence consists of a predicate applied to one or more terms; it is true just when the relation named by the predicate holds between the objects named by the terms. Complex sentences use connectives just like propositional logic, and quantified sentences allow the expression of general rules. Developing a knowledge base in first-order logic requires a careful process of analyzing the domain, choosing a vocabulary, and encoding the axioms required to support the desired inferences.

4 GTTTOT in First-Order Logic case(8, 8, downtown, good). distance(D) :- case(D, _, _, _). time(T) :- case(_, T, _, _). location(L) :- case(_, _, L, _). weather(W) :- case(_, _, _, W). means(X) :- distance(D), D > 5, X = drive. means(X) :- distance(D), D > 1, time(T), T < 15, X = drive. means(X) :- distance(D), D > 1, time(T), T > 15, X = walk. action(X) :- means(drive), location(downtown), X = takecab. action(X) :- means(drive), not location(downtown), X = drivecar. action(X) :- means(walk), weather(good), X = walk. action(X) :- means(walk), weather(bad), X = walkwithcoat.

5 The Semantic Web Tim Berners-Lee’s original vision of the Web was more than the information space that we see today. In his own words: “The Web was designed as an information space, with the goal that it should be useful not only for human-human communication, but also that machines would be able to participate and help. One of the major obstacles to this has been the fact that most information on the Web is designed for human consumption, and even if it was derived from a database with well defined meanings (in at least some terms) for its columns, that the structure of the data is not evident to a robot browsing the web. Leaving aside the artificial intelligence problem of training machines to behave like people, the Semantic Web approach instead develops languages for expressing information in a machine processable form.”

6 Semantic Web Resources What is a ‘Semantic Web’? W3C Characterization of Semantic Web Presentation by Tim Berners-Lee Scientific American Article Semantic Web: Info, Tools, Techniques

7 OIL & OWL Semantic Web Ontology Languages OIL - Ontology Inference Layer. OWL - Web Ontology Language. The 3 roots of OIL & OWL: Web languages (XML, RDF) Frame based systems Description logics OIL & OWL Resources: OIL Home Page OIL: An Ontology Infrastructure for the Semantic Web OIL: An Ontology Infrastructure for the Semantic Web OWL: The Web Ontology Language W3C Web Ontology Language Guide Introduction to OWL 1 Introduction to OWL 2


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