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Improving Semantic Integration by Learning
Improving Semantic Integration by Learning

... First we explain a baseline system that can be used in the presence of a parser, knowledge base and word to concept mapping. First the dependency tree is transformed into a simplified syntactic graph. The noun phrases and verbs are identified, these form the nodes in the syntactic graph. Then the pa ...
CogSketch: Sketch Understanding for Cognitive Science Research
CogSketch: Sketch Understanding for Cognitive Science Research

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Data mining

... ---What kinds of patterns can be mined? Data mining functionalities are used to specify the kinds of patterns to be found in data mining tasks. Data mining tasks can be classified into two categories: ...
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(PPT, 221KB)

... In Artificial Intelligence, an expert system is a computer system that emulates the decisionmaking ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as IF-THEN rules rather than through conventional procedural code. T ...
Measurements of collective machine intelligence
Measurements of collective machine intelligence

... ntelligence is a fairly intuitive concept of our everyday life. As usually acIgence knowledged in psychometrics, “intelligence is the ability measured by intellitests”. However, defining what exactly intelligence tests should measure is less obvious. During the last decade, computer scientists have ...
Laboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica

... ‰ Machine Learning Write a program that can learn how to play It can learn from examples of previous games, by playing against another opponent, by playing against itself ...
Logical Formal Description of Expert Systems
Logical Formal Description of Expert Systems

Machine Ethics, the Frame Problem, and Theory of Mind
Machine Ethics, the Frame Problem, and Theory of Mind

ai-ready or not: artificial intelligence here we
ai-ready or not: artificial intelligence here we

... In its most basic definition, “artificial intelligence” (AI) is intelligence that is exhibited by machines. It is frequently thought of as robotics but it actually encompasses a broader range of technologies, including many that are in wide use today. From speech recognition and search engines, to o ...
Bayesian Ontologies in AI Systems - Department of Information and
Bayesian Ontologies in AI Systems - Department of Information and

... gaming, but each closed loophole spurs innovation to discover another. Additionally, the infeasibility of enforcing a single global standard ontology means that semantic interoperability will continue to be a difficult objective to achieve in an environment in which capabilities are controlled by a ...
Logical Agents
Logical Agents

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An Overview of Some Recent Developments in Bayesian Problem
An Overview of Some Recent Developments in Bayesian Problem

... user models that continue to infer a user's goals and needs by considering the user’s background, actions, and queries. The approach taken is to develop Bayesian user models that capture the uncertain relationships among the goals and needs of a user and observations about program state, sequences o ...
Management Information Systems Chapter 12
Management Information Systems Chapter 12

... behaviors in large data sets, using techniques such as neural networks and data mining Artificial Intelligence (AI) technology: • Computer-based systems based on human behavior, with the ability to learn languages, accomplish physical tasks, use a perceptual apparatus, and emulate human expertise an ...
How to Reason by HeaRT in a Semantic Knowledge-Based Wiki
How to Reason by HeaRT in a Semantic Knowledge-Based Wiki

... system and its extensions. They allow to assign categories and attributes to wiki pages and define semantic relations among them; they facilitate navigation as well as enable posing semantic queries. The semantically annotated knowledge can be aggregated and queried, and simple classification tasks ...
Recognition Tasks
Recognition Tasks

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Framed - Alison Goodman
Framed - Alison Goodman

... Artificial intelligence problems are usually played out in a simplified environment where the robot or computer is set well defined tasks within a well defined environment. The robot has a reduced set of aspects to exercise its reduced set of axioms on. This method of investigating intelligence is, ...
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... capabilities can improve the performance of an intelligent system over time. The most popular approaches to machine learning are artificial neural networks and genetic algorithms. This lecture is dedicated to neural networks. ...
CS 540 * Introduction to AI Fall 2015
CS 540 * Introduction to AI Fall 2015

... (also on course homepage) All examinations, programming assignments, and written homeworks must be done individually. Cheating and plagiarism will be dealt with in accordance with ...
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... deep learning. His early work includes the Stanford Autonomous Helicopter project, which developed one of the most capable autonomous helicopters in the world, and the STAIR (STanford Artificial Intelligence Robot) project, which resulted in ROS (Robot Operating System)|ROS, a widely used open sourc ...
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... Idea as in Cook’s proof of NP-hardness of SAT [Coo71]: encode each step in a plan as a propositional formula. ...
Improving the Knowledge-Based Expert System Lifecycle
Improving the Knowledge-Based Expert System Lifecycle

... one manifestation of the applications that trace their roots back to those early programs. Knowledge-based expert systems are computer systems that have expertise in a given domain and are useful when analyzing and processing large amounts of data in a short amount of time [Grosan11] [Dabbaghchi97]. ...
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... Disagreement may occur because of incorrect pairing of views. Multi-view learning with these pairings leads to corrupted foreground class models. Following the approach from Christoudias, M., Urtasun, R., and Darrell, T. 2008. Multi-View Learning in the Presence of View Disagreement. 9 pp., In Proce ...
CS 561a: Introduction to Artificial Intelligence
CS 561a: Introduction to Artificial Intelligence

... • Example: Human mind as network of thousands or millions of agents working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also contain many agents and systems for arbitrating among the agents' competing results. Agency ...
Higher Course Specification
Higher Course Specification

... sense’, need for expertise to set up and maintain, inability to acquire new knowledge, inflexibility) Description of moral issues (including medical implications) Description of legal issues (including responsibility when advice is wrong) Search techniques Comparison of depth-first and breadth-first ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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