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Image processing --- Optical pattern recognition --- Machine learning

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Mathematical statistics --- Machine learning --- Data processing --- Congresses.

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Artificial intelligence. Robotics. Simulation. Graphics --- Machine Learning --- Computer algorithms --- 681.3*I26 --- 681.3*K32 --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Computer and information science education: curriculum; self-assessment --- Basic Sciences. Statistics --- Statistics (General) --- Computer algorithms. --- Machine learning. --- Artificiële intelligentie --- Computeralgoritmen --- Machineleren --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*K32 Computer and information science education: curriculum; self-assessment --- Statistics (General). --- Artificiële intelligentie. --- Computeralgoritmen. --- Machineleren. --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Algorithms --- Algorithmes --- Apprentissage automatique

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The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.

Machine learning --- Distributed artificial intelligence --- Computer Science --- Engineering & Applied Sciences --- 681.3*I2 <063> --- Learning, Machine --- Artificial intelligence --- Machine theory --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Artificial intelligence. AI--Congressen --- 681.3*I2 <063> Artificial intelligence. AI--Congressen --- Computer science. --- Programming languages (Electronic computers). --- Artificial intelligence. --- Computer simulation. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Simulation and Modeling. --- Programming Languages, Compilers, Interpreters. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Languages, Artificial --- Informatics --- Science --- Congresses --- Artificial Intelligence. --- Machine learning - Congresses --- Distributed artificial intelligence - Congresses --- Programming languages (Electronic computers)

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Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.

Mathematical logic --- Theory of knowledge --- Logic programming --- Logisch programmeren --- Logische programmering --- Programmation logique --- Programmeren [Logisch ] --- Programming [Logic ] --- Logic programming. --- Machine learning. --- Induction (Logic) --- Machine learning --- Computer Science --- Engineering & Applied Sciences --- 681.3*D16 --- 681.3*F41 --- 681.3*I26 --- Learning, Machine --- Artificial intelligence --- Machine theory --- Computer programming --- Inductive logic --- Logic, Inductive --- Logic --- Reasoning --- Computerwetenschap--?*D16 --- Mathematical logic: computability theory; computational logic; lambda calculus; logic programming; mechanical theorem proving; model theory; proof theory;recursive function theory--See also {681.3*F11}; {681.3*I22}; {681.3*I23} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*F41 Mathematical logic: computability theory; computational logic; lambda calculus; logic programming; mechanical theorem proving; model theory; proof theory;recursive function theory--See also {681.3*F11}; {681.3*I22}; {681.3*I23} --- Computer science. --- Software engineering. --- Computer programming. --- Mathematical logic. --- Artificial intelligence. --- Computer Science. --- Software Engineering/Programming and Operating Systems. --- Artificial Intelligence (incl. Robotics). --- Mathematical Logic and Formal Languages. --- Programming Techniques. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algebra of logic --- Logic, Universal --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Computer software engineering --- Engineering --- Informatics --- Science --- Programming --- Artificial Intelligence. --- Logic, Symbolic and mathematical.