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20 July 2010 100 hour course period 5 artificial Intelligence and
20 July 2010 100 hour course period 5 artificial Intelligence and

... artificial Intelligence and automated systems Create a heading Key Terms and write a definition for the following terms. Also add a tube or image of each where possible. Agent: in the client-server model, the part of the system that performs information preparation and exchange on behalf of a client ...
slides
slides

... IF (on bus AND paid AND empty seat) THEN sit down ...
Artificial Intelligence
Artificial Intelligence

... symbol hypothesis of Newell and Simon (1981). • Essentially this hypothesis considers that knowledge consists of symbols of reality and relationships between these symbols and that intelligence is the appropriate logical manipulation of the symbols and their relations. • Although Newell an Simon dev ...
Artificial Intelligence
Artificial Intelligence

... which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed. ...
CS 415 – A.I.
CS 415 – A.I.

... • Predicates are not functions in the sense of higher-level languages, nor should you think of them in terms of programming – There is no set of predicate functions – Any predicate can be defined • They are strictly useful for representing knowledge in conjunction with rules ...
PowerPoint Presentation - Computing Science
PowerPoint Presentation - Computing Science

... Class frames describe classes. Instance frames describe instances. Each frame has a number of slots. Each slot can be assigned a slot value. ...
Revision Lectures - School of Computer Science
Revision Lectures - School of Computer Science

... concepts and overall grasp of main examples.) But of course knowledge of all the above types could be helpful and impressive. ...
What is AI? Acting humanly: The Turing test Thinking humanly
What is AI? Acting humanly: The Turing test Thinking humanly

... ♦ Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes ♦ Anticipated all major arguments against AI in following 50 years ♦ Suggested major components of AI: knowledge, reasoning, language understanding, learning Problem: Turing test is not reproducible, co ...
Knowledge Based Systems II
Knowledge Based Systems II

... substitution because of the inherent ambiguity of natural languages. To resolve this ambiguity requires contextual knowledge in the form of a world model. As an example, a punctuation symbol “,” can have the meaning of a list separator, or be used in place of “then”, or be used to delimit a dangling ...
General Problem Solving
General Problem Solving

... The goal is to simulate the solving problem process of human experts Their development is based on traditional knowledge engineering techniques Mainly implemented with rule production systems Closed applications that usually do not use machine learning Knowledge Based Systems The goal is to use doma ...
CS 431: ARTIFICIAL INTELLIGENCE Today
CS 431: ARTIFICIAL INTELLIGENCE Today

... 1950s: Early AI programs including Samuel’s checkers program, Newell & Simon’s Logic theorist, Gelernter’s Geometry Engine 1956: Dartmouth meeting, “Artificial Intelligence” adopted 1965: Robinson’s complete algorithm for logical reasoning ...
15. MANAGING KNOWLEDGE
15. MANAGING KNOWLEDGE

...  May be merged with expert systems – e.g., expert system could select -- neural network could monitor  IS specialists may need to be proficient in neural network skills  Neural network applications may increase with greater computing power  Large interconnected neural network applications will b ...
Artificial Intelligence 4. Knowledge Representation
Artificial Intelligence 4. Knowledge Representation

... Wrong choice can lead to project failure Active research area ...
Future and Emerging Technologies FET
Future and Emerging Technologies FET

... all formalized knowledge; for prototype estimated completion date is M18). This integration has been initiated on a simplified class diagram in the system provided in 3.1. This integration is being done over the Matlab software tool. Accordingly, we are currently investigating methods to integrate o ...
Encyclopedia of Artificial Intelligence
Encyclopedia of Artificial Intelligence

... This, in turn, may lead to the generation of a new derived fact or an action. Rule1 and rule2 are interdependent, since the conclusion of one can satisfy the condition of the other. The inter-dependencies amongst the rules define a network, as shown in Figure 2, known as an inference network. It is ...
1 - Department of Computer Science
1 - Department of Computer Science

... It’s engineering.” (J. Moore, Wired) ...
Knowledge Based Systems
Knowledge Based Systems

... Knowledge Based Systems and Artificial Intelligence Intelligent Knowledge Based Systems and Expert Systems ...
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Document

... • No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) • During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people ...
PARKA: A System for Massively Parallel Knowledge Representation
PARKA: A System for Massively Parallel Knowledge Representation

... PARKA is a frame-based knowledge representation system implemented on massively parallel hardware--the Connection Machine (CM-2). PARKA provides a representation language consisting of concept descriptions (frames) and binary relations on those descriptions (slots). The system is designed explicitly ...
Call for Papers - Southern Illinois University
Call for Papers - Southern Illinois University

... Intelligent agent technology is emerging as one of the most rapidly advancing areas in information technology. There is a fast pace in the conceptualization and development of agent-based applications in a variety of fields such as electronic commerce, supply chain management, resource allocation, p ...
1 - El
1 - El

... separate things: mind (or soul) and matter (or body). Descartes did not believe that this dualism extended to animals. Natural Language Processing came from the writings of Noam Chomsky, who in the 1950s proposed his theory of Syntactic Structures. McCulloch and Pitts’s electronic neurons, which are ...
AIIntro
AIIntro

... problems of finding solutions to difficult problems Solutions are are found by analyzing competing solutions; solutions with promise will tend to survive ...
What is computing? Counting, calculating The discipline of
What is computing? Counting, calculating The discipline of

... Nerve cells function about 106 times slower than electronic circuit gates, but human brains process visual and auditory information much faster than modern computers The brain is modeled as a continuous-time non linear dynamic system in connectionist architectures • Connectionism replaced symbolical ...
Intelligent DSS - Telkom University
Intelligent DSS - Telkom University

... Automatic programming Pattern recognition Any can furnish mechanisms for artificially intelligent DSSs IF-UTAMA ...
I Had a Dream: AAAI Presidential Address, 19 August 1985
I Had a Dream: AAAI Presidential Address, 19 August 1985

... It is believed that such a large-structured knowledge base would not only allow the sharing of knowledge by numerous systems, but, if structured correctly, could provide much more robustness and functionality than is possible from a number of distinct smaller KBs. It has been said that we cannot hav ...
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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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