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The author or concept searched is found in the following 3 entries.
| Disputed term/author/ism | Author |
Entry |
Reference |
|---|---|---|---|
| Categories | AI Research | Norvig I 440 Categories/Ai research/Norvig /Russell: The organization of objects into categories is a vital part of knowledge representation. Although interaction with the world takes place at the level of individual objects, much reasoning takes place at the level of categories. Categories also serve to make predictions about objects once they are classified. One infers the presence of certain objects from perceptual input, infers category membership from the perceived properties of the objects, and then uses category information to make predictions about the objects. >Knowledge representation/AI research, >Ontology/AI research. There are two choices for representing categories in first-order logic: predicates and objects. Inheritance: Categories serve to organize and simplify the knowledge base through inheritance. If we say that all instances of the category Food are edible, and if we assert that Fruit is a subclass of Food and Apples is a subclass of Fruit, then we can infer that every apple is edible. We say that the individual apples inherit the property of edibility, in this case from their membership in the Food category. Norvig I 454 Reasoning systems for categories: a) semantic networks: use labels like male/female, “mother/father etc. The semantic network notation makes it convenient to perform inheritance reasoning (…). Norvig I 455 Inheritance: becomes complicated when an object can belong to more than one category or when a category can be a subset of more than one other category; this is called multiple inheritance. In such cases, the inheritance algorithm might find two or more conflicting values answering the query. For this reason, multiple inheritance is banned in some object-oriented programming (OOP) languages, such as Java, that use inheritance in a class hierarchy. It is usually allowed in semantic networks (…) Norvig I 456 Description logic: Description logics are notations that are designed to make it easier to describe definitions and properties of categories. The principal inference tasks for description logics are subsumption (checking if one category is a subset of another by comparing their definitions) and classification (checking whether an object belongs to a category). Norvig I 456 VsDescription logics/Norvig: either hard problems cannot be stated at all, or they require exponentially large descriptions! ((s) For a solution see >Conceptual space/Gärdenfors; >Semantic Web/Gärdenfors. (GärdenforsVsRussell, Stuart/GärdenforsVsNorvig). >Description logic/AI research. |
Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 |
| Description Logic | AI Research | Norvig I 456 Description logic/AI research/Russell/Norvig: Description logics are notations that are designed to make it easier to describe definitions and properties of categories. The principal inference tasks for description logics are subsumption (checking if one category is a subset of another by comparing their definitions) and classification (checking whether an object belongs to a category). Norvig I 456 VsDescription logics/Norvig: either hard problems cannot be stated at all, or they require exponentially large descriptions! ((s) For a solution see >Conceptual space/Gärdenfors; >Semantic Web/Gärdenfors. (GärdenforsVsRussell, Stuart/GärdenforsVsNorvig). Norvig I 459 Circumspription: The idea is to specify particular predicates that are assumed to be “as false as possible”—that is, false for every object except those for which they are known to be true. For example, suppose we want to assert the default rule that birds fly. We would introduce a predicate, say Abnormal 1(x), and write Bird(x) ∧¬Abnormal 1(x) ⇒ Flies(x) . If we say that Abnormal 1 is to be circumscribed, a circumscriptive reasoner is entitled to assume ¬Abnormal 1(x) unless Abnormal 1(x) is known to be true. This allows the conclusion Flies(Tweety) to be drawn from the premise Bird(Tweety ), but the conclusion no longer holds if Abnormal 1(Tweety) is asserted. Circumscription can be viewed as an example of a model preference logic. In such logics, a sentence is entailed (with default status) if it is true in all preferred models of the knowledge base, as opposed to the requirement of truth in all models in classical logic. Norvig I 471 The development of description logics is the most recent stage in a long line of research aimed at finding useful subsets of first-order logic for which inference is computationally tractable. Hector Levesque and Ron Brachman (1987)(1) showed that certain logical constructs - notably, certain uses of disjunction and negation - were primarily responsible for the intractability of logical inference. Building on the KL-ONE system (Schmolze and Lipkis, 1983)(2), several researchers developed systems that incorporate theoretical complexity analysis, most notably KRYPTON (Brachman et al., 1983)(3) and Classic (Borgida et al., 1989)(4). The result has been a marked increase in the speed of inference and a much better understanding of the interaction between complexity and expressiveness in reasoning systems. Calvanese et al. (1999)(5) summarize the state of the art, and Baader et al. (2007)(6) present a comprehensive handbook of description logic. Against this trend, Doyle and Patil (1991)(7) have argued that restricting the expressiveness of a language either makes it impossible to solve certain problems or encourages the user to circumvent the language restrictions through nonlogical means. >Inference/AI research. 1. Levesque, H. J. and Brachman, R. J. (1987). Expressiveness and tractability in knowledge representation and reasoning. Computational Intelligence, 3(2), 78–93. 2. Schmolze, J. G. and Lipkis, T. A. (1983). Classification in the KL-ONE representation system. In IJCAI-83, pp. 330–332. 3. Brachman, R. J., Fikes, R. E., and Levesque, H. J. (1983). Krypton: A functional approach to knowledge representation. Computer, 16(10), 67–73. 4. Borgida, A., Brachman, R. J., McGuinness, D., and Alperin Resnick, L. (1989). CLASSIC: A structural data model for objects. SIGMOD Record, 18(2), 58-67. 5. Calvanese, D., Lenzerini, M., and Nardi, D. (1999). Unifying class-based representation formalisms. JAIR, 11, 199–240 6. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and Patel-Schneider, P. (2007). The Description Logic Handbook (2nd edition). Cambridge University Press. 7. Doyle, J. and Patil, R. (1991). Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services. AIJ, 48(3), 261–297. |
Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 |
| Models | Norvig | Pariser I 213 Models/Norvig/Pariser: Norvig thesis: All models are wrong and you will be more and more successful without them.(1) PariserVsNorvig: Machinery can deliver results without models, but people cannot understand them without models. >Understanding, >Models/Duhem, >Model theory. 1. Quoted in: Chris Anderson, »The End of Theory: The Data Deluge Makes the Scientific Method Obsolete«, Wired, 23. 06. 2008, aufgerufen am 10. 02. 2010, http://www.wired.com/science/discoveries/magazine/16-07/pb_theory. |
Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 Pariser I Eli Pariser The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think London 2012 |
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