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symbolic logic and logic processing
symbolic logic and logic processing

... and linguistics. The aim of AI is broad: to get below the surface of human behaviour; to discover the processes, systems and principles that make intelligent behaviour possible. There are many practical applications of AI which include the design of computer systems that can perceive, learn, solve p ...
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... Some references state that term "robot" was derived from the Czech word robota, meaning "work", while others propose that robota actually means "forced workers" or "slaves." This latter view would certainly fit the point that Capek was trying to make, because his robots eventually rebelled against t ...
Study of Nature Inspired Computing
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... respond slowly but implement much higher-level operations. The ability of biological systems to assemble and grow on their own enables much higher interconnection densities.[10] One of the most inspiring natural intelligence is the human mind itself. There are many theories of how minds work. This i ...
Visualizing Inference Henry Lieberman and Joe Henke MIT Media Lab
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... There is surprisingly little work on visualization of inference in AI. Visualization tools are often used on concept ontologies [Protègé 14], [Katifori 07] (but not on associated inference of assertions). Specialized inference algorithms provide visualization of their internal data structures [Cossa ...
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Abstract - NYU Computer Science
Abstract - NYU Computer Science

... The importance of real-world knowledge for natural language processing, and in particular for disambiguation of all kinds, was discussed as early as 1960, by Bar-Hillel (1960), in the context of machine translation. Although some ambiguities can be resolved using simple rules that are comparatively ...
Inferential Knowledge of the Occurrence of Something
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... an inferential knowledge concerning the occurrence of something. In one passage, in particular, the subject under discussion are the mental qualities. The paper will expose the use of saṃbhavānumāna in Dharmakīrti’s writings and the commentaries upon them, examining whether and in what way both the ...
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... By the end of the course, students will be able to • To understand the concepts of semantic web technology • Semantic web services and applications • RDF,OWL,UDDI,OWL-S,WSDL-S technologies Traditional web to semantic web – meta data- search engines – Resource Description Framework –elements – rules ...
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Progress and Challenges in Interactive Cognitive Systems
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... •  The ability to encode, manipulate, and interpret symbol structures is necessary and sufficient for general intelligent action. •  Problem solving involves heuristic search through a space of states (symbol structures) generated by mental operators. We offer a third claim – the social cognition hy ...
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... Inference with Horn clauses can be done thru forward and backward chaining  Forward chaining is data driven  Backward chaining works backwards from the query, goal- ...
Approaches to Artificial Intelligence
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... combinations of these approaches will be required. In any case, the advocates of tllese approaches often feel that theirs is the "breakthrough" methodology that deserves special support. In order to acquaint researchers and others with these paradigms and their principal results, the Santa Fe Instit ...
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... is used when the patient’s data are entered without guidance by the computer. Those rules whose premises match the data are then applied, and new rules that use the conclusions in their premise conditions are subsequently applied, etc. Instead of using one of the two strategies, it is also possible ...
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... how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered “somewhat intelligent”. Q. Isn’t AI about simulating human ...
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... systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recog ...
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... an overall semantic net in order, (or example, to limit or group information relevant t.o the current problem or sub-problem, or to provide a represent.ation of t.he meaning of the text or utterance currently being processed, whereas each module is intended to be sufficient by itself - a largely ind ...
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Intelligent Behavior in Humans and Machines
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... knowledge about the world, the processes that they employ to retrieve and use that knowledge, and the mechanisms by which they acquire it from experience. AI researchers must make decisions about these issues when designing intelligent systems, and psychological results about representation, perform ...
evolutionary computation
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... intelligence, adaptability, cellular or connectionist systems, evolutionary systems etc, the classical notion of "problem solving" remains valid as—in its most abstract and general form—it is encountered in virtually any AI system. For instance, adaptability can be conceived as convergence to goal-s ...
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Knowledge representation and reasoning

Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.Examples of knowledge representation formalisms include semantic nets, systems architecture, Frames, Rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers.
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