Preference-Driven Querying of Inconsistent Relational Databases ⋆
... Example 2. The instance r of Example 1 has 3 repairs: r1 = {(Mary, R&D, 40k, 3), (John, PR, 30k, 4)}, r2 = {(John, R&D, 10k, 2), (Mary, IT, 20k, 1)}, r3 = {(Mary, IT, 20k, 1), (John, PR, 30k, 4)}. Because Q1 is false in r1 and r2 , true is not a consistent answer to Q1 . The standard framework of co ...
... Example 2. The instance r of Example 1 has 3 repairs: r1 = {(Mary, R&D, 40k, 3), (John, PR, 30k, 4)}, r2 = {(John, R&D, 10k, 2), (Mary, IT, 20k, 1)}, r3 = {(Mary, IT, 20k, 1), (John, PR, 30k, 4)}. Because Q1 is false in r1 and r2 , true is not a consistent answer to Q1 . The standard framework of co ...
Drawing Clustered Graphs - School of Information Technologies
... Data sets are growing at a faster rate than the human ability to understand them. ...
... Data sets are growing at a faster rate than the human ability to understand them. ...
Machine Learning for Computer Games
... you out think it the second, third and forth? • “Check out the revolutionary A.I. Drivatar™ technology: Train your own A.I. "Drivatars" to use the same racing techniques you do, so they can race for you in competitions or train new drivers on your team. Drivatar technology is the foundation of the h ...
... you out think it the second, third and forth? • “Check out the revolutionary A.I. Drivatar™ technology: Train your own A.I. "Drivatars" to use the same racing techniques you do, so they can race for you in competitions or train new drivers on your team. Drivatar technology is the foundation of the h ...
Contents | Zoom in | Zoom out Search Issue | Next Page For
... omputational intelligence is at the heart of many new technological developments. For example, recently there are a lot of deliberations, even in popular media such as The New York Times, about the need to handle Big Data. This is an area that the industry is particularly interested in, with huge po ...
... omputational intelligence is at the heart of many new technological developments. For example, recently there are a lot of deliberations, even in popular media such as The New York Times, about the need to handle Big Data. This is an area that the industry is particularly interested in, with huge po ...
PDF
... Representative Program Selection A benchmark suite aims to represent a wide selection of programs with a small subset of benchmarks. By its very nature a benchmark suite is therefore a statistical sample of the application space. Creating a selection of benchmarks by choosing samples of applications ...
... Representative Program Selection A benchmark suite aims to represent a wide selection of programs with a small subset of benchmarks. By its very nature a benchmark suite is therefore a statistical sample of the application space. Creating a selection of benchmarks by choosing samples of applications ...
Keynote ICSD 2009 Digital Libraries and the
... • Support arrangement and sequencing of material to be learned • Tools for ontology construction by the learner, for example, concept maps Active learning, building own structures, constructivist approach Soergel, ICSD 2009 Keynote ...
... • Support arrangement and sequencing of material to be learned • Tools for ontology construction by the learner, for example, concept maps Active learning, building own structures, constructivist approach Soergel, ICSD 2009 Keynote ...
Evolution of Reward Functions for Reinforcement Learning applied
... certain objective without being spotted by enemy patrols. This gave rise to a new genre called stealth games, where covertness plays a major role. Although quite popular in modern games, stealthy behaviors has not been extensively studied. In this work, we tackle three different problems: (i) how to ...
... certain objective without being spotted by enemy patrols. This gave rise to a new genre called stealth games, where covertness plays a major role. Although quite popular in modern games, stealthy behaviors has not been extensively studied. In this work, we tackle three different problems: (i) how to ...
Artificial Intelligence Illuminated
... intended to be an interesting and relevant introduction to the subject for other students or individuals who simply have an interest in the subject. The book assumes very little knowledge of computer science, but does assume some familiarity with basic concepts of algorithms and computer systems. Da ...
... intended to be an interesting and relevant introduction to the subject for other students or individuals who simply have an interest in the subject. The book assumes very little knowledge of computer science, but does assume some familiarity with basic concepts of algorithms and computer systems. Da ...
INDEX LESSON 1: INTRODUCTION TO DATA PROCESSING
... The output stage of computing is concerned with giving out processed data as information in a form that is useful to the user. Output devices are used to do this. The most commonly used output devices are the screen, which is also called a monitor or VDU and the printer. 1.3. Architecture of Compute ...
... The output stage of computing is concerned with giving out processed data as information in a form that is useful to the user. Output devices are used to do this. The most commonly used output devices are the screen, which is also called a monitor or VDU and the printer. 1.3. Architecture of Compute ...
TOWARDS A MENTAL PROBABILITY LOGIC Niki PFEIFER
... Conditional and preferential entailment: conditional logic-based (Delgrande, 1988; Schurz, 1998), preferential model-based (Kraus, Lehmann, & Magidor, 1990; Lehmann & Magidor, 1992), expectation-ordering-based (Gärdenfors & Makinson, 1994), and probabilistic entailment-based approaches (Gilio, 2002; ...
... Conditional and preferential entailment: conditional logic-based (Delgrande, 1988; Schurz, 1998), preferential model-based (Kraus, Lehmann, & Magidor, 1990; Lehmann & Magidor, 1992), expectation-ordering-based (Gärdenfors & Makinson, 1994), and probabilistic entailment-based approaches (Gilio, 2002; ...