INTELIGENCIA ARTIFICIAL STUART RUSSELL PDF
Artificial intelligence: a modern approach/ Stuart Russell, Peter Norvig. p. cm. There are many textbooks that offer an introduction to artificial intelligence (AI). Cover Image Creation: Stuart Russell and Peter Norvig; Tamara Newnam and Patrice Van Acker Artificial Intelligence (AI) is a big field, and this is a big book. Veja grátis o arquivo Artificial-Intelligence-A-Modern-Approach-3rd-Edition - Stuart J. Russell and Peter resourceone.info enviado para a disciplina de Inteligência.
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Request PDF | On Jan 1, , Stuart Russell and others published Artificial de las habilidades humanas, planteado así la inteligencia artificial -se invita a. Request PDF on ResearchGate | Artificial Intelligence: A Modern Approach / S.J. Russell, P. Norvig. | Obra en que se explica la inteligencia artificial a través de la definición de agentes inteligentes y las funciones de Stuart Jonathan Russell. The following translations of Artificial Intelligence: A Modern Approach are available: By Stuart Russell and Peter Norvig. Prentice Hall,
The ar- ticles can roughly be grouped into the two categories: theoretical developments and practical applications.
The first group deals with issues in granular comput- ing, learning methods, evolutionary optimization, swarm intelligence, linguistic dynamic systems, neurocomputing, and neuro-fuzzy computing; while the sec- ond group concerns with applications in motif identification in DNA and protein sequences, adaptive approximation for nonlinear functions, neuromuscular con- trol, reverse engineering of protein and gene networks, biometric problems, and intelligent control.
Granular computing is one of the key components in the current study of com- putational intelligence.
In Chapter 1, Pedrycz investigates the role of granular Preface vn computing and logic processing in the context of intelligent systems and demon- strates that both of them are organized into a single conceptual and computational framework. This chapter provides a general overview of granular computing that emphasizes a diversity of the currently available concepts and underlines their common features that make the entire pursuit highly coherent.
The logic facet of processing is cast in the realm of fuzzy logic and fuzzy sets that construct a consis- tent processing background necessary for operating on information granules. The synergetic links between granular and logic as well as several main categories of logic processing units or logic neurons have been examined in order to show how they contribute to high functional transparency of granular processing, help cap- ture prior domain knowledge and give rise to a diversity of the resulting models.
This work represents a significant step towards the establishment of a framework that uses granular computing as a fundamental environment supporting the de- velopment of intelligent systems. Chapter 2 addresses another important issue in granular computing, that is, the abstraction of conventional dynamic systems for the purpose of conducting linguistic analysis based on numerical representation.
To this end, the concepts and methods developed in linguistic dynamic systems LDS by Wang are utilized. Specifically, conventional dynamic systems are con- verted to different types of LDS for the purpose of verification and comparison. The evolving laws of a type-I LDS are constructed by applying the fuzzy exten- sion principle to those of its conventional counterpart with linguistic states.
In addition to linguistic states, the evolving laws of type-II LDS are modeled by a finite number of linguistic decision rules. Analysis of fixed points is conducted based on point-to-fuzzy-set mappings and linguistic controllers are designed for goals specified in words for type-II LDS.
An efficient numerical procedure called a-cuts mapping is developed and applied for simulation studies. Ever-increasing dataset sizes and ever-larger problems have spurred research into efficient learning methods.
Eschrich and Hall present in Chapter 3 a new learning algorithm along this direction, called slicing, that embodies the principles of distributed learning, not only to simplify a learning problem overall but also to simplify the individual learning tasks. Slicing, or partitioning and learning, can be seen as a method in which the training set is partitioned into disjoint regions of feature space and treated as a set of learning tasks that can be run in a distributed environment without extensive communication.
This chapter examines slicing algorithm with respect to a series of real-world datasets, including a biologically- motivated problem, and shows that it can be used as a general meta-learning tech- nique for distributed learning.
Clearly, slicing is more accurate than using a single classifier, can reduce the individual learning task size and provides a mechanism to distribute the data mining task.
Chapter 4 discusses margin methods for both supervised and unsupervised learning problems. For supervised learning, a com- Vlll Preface plete framework of posterior probability support vector machines PPSVMs is proposed for weighted training samples using modified concepts of risks, linear separability, margin and optimal hyperplane.
Within this framework, a new opti- mization problem for unbalanced classification problems is formulated and a new concept of support vectors is established. Tao and Wang extend the margin idea further to unsupervised learning problems and establish a universal framework for one-class, clustering and PCA principal component analysis problems.
Optimization using evolutionary computing is a very important field of stud- ies. In Chapter 5, Yen describes a generic, two-phase framework for solving con- strained optimization problems using genetic algorithms. In the first phase of the algorithm, the objective function is completely disregarded and the constrained optimization problem is treated as a constraint satisfaction problem. The genetic search is directed toward minimizing the constraint violation of the solutions and eventually finding a feasible solution.
In the second phase the simultaneous opti- mization of the objective function and the satisfaction of the constraints are treated as a bi-objective optimization problem. The proposed algorithm is analyzed under different problem scenarios using Test Case Generator-2 and its capability to per- form well independent of various problem characteristics is demonstrated.
Note that Yen's algorithm performs competitively with the state-of-the-art constraint optimization algorithms on eleven test cases which were widely studied bench- mark functions in the literature. Neuro-evolutionary computing, neuro-fuzzy computing, and swarm intelli- gence are the subject of many current research works in computational intelli- gence.
In Chapter 6, Cai, Venayagamoorthy, and Wunsch investigate a hybrid training algorithm based on particle swarm optimization PSO and evolutionary algorithm EA for feedforward and recurrent neural networks. Particularly, the applications of the hybrid PSO-EA algorithm for training of a feedforward neu- ral network as a board evaluator for the game Go and for training of a recurrent neural network to predict the missing values from a time series are presented to show its potential.
Results indicate that the proposed hybrid algorithm performs better than individual application of PSO or EA algorithm as a training algorithm. In Chapter 7, Lin and Wang present a novel approach to combining wavelet net- works and multilayer feedforward neural networks for fuzzy logic control sys- tems. While most of the existing neuro-fuzzy systems focus on implementing the Takagi-Sugano-Kang TSK fuzzy inference model, they fail to keep the knowl- edge structure that is critical in interpreting the learning process and providing in- sights to the working mechanism of the underlying systems.
It is their intention to utilize individual subnets to implement decision-making process of the fuzzy logic control systems based on the Mamdani model.
Center average defuzzification has been implemented by a neural network so that a succinct network structure is Preface IX obtained. More importantly, wavelet networks have been adopted to provide better locality capturing capability and therefore better performance in terms of learning speed and training time.
Ant colony algorithms are the pioneers of swarm intelli- gence. Ant colony optimization ACO , a new meta-heuristic method based on the observation of real ant colony activities and behaviors, offers a new way to solve many complicated optimization problems. Bioinformatics is an exciting and important area of applications for compu- tational intelligence. Liu and Xiong start applications with the problem of motif discoveries in unaligned DNA and protein sequences in Chapter 9.
Current popu- lar algorithms for this problem face two difficulties: high computational cost and the possibility of insertions and deletions. This chapter proposes a self-organizing neural network structure as a new solution.
This network contains several sub- networks with each performing classifications at different levels. The top level divides the input space into a small number of regions and the bottom level clas- sifies all input patterns into motifs and non-motif patterns. A low computational complexity is maintained through the use of the layered structure so that each pat- tern's classification is performed with respect to a small subspace of the whole input space.
Note that simu- lation results show that their algorithm can identify motifs with more mutations than existing algorithms and their algorithm works well for long DNA sequences as well. In Chapter 10, Berman, DasGupta, and Sontag present an interesting in- vestigation on some computational problems that arise in the reverse engineering of protein and gene networks. They discuss the biological motivations, provide precise formulations of the combinatorial questions that follow from these moti- vations and then describe their computational complexity issue, namely efficient approximation algorithms for these problems.
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Técnicas de inteligencia artificial (2010). OpenCourseWare UA
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309173562 Inteligencia Artificial 3a Ed Russell Stuart Norvig Peter PDF
If we catch you copy such solutions, you automatically fail. Do not bother to obfuscate plagiarism e. One application of AI is to develop sophisticated plagiarism detection techniques!
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Professor Zhu will work either directly with the student or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Additional Course Information Course learning outcomes: Students gain principles of knowledge-based search techniques; automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning.Inteligencia Artificial: Center average defuzzification has been implemented by a neural network so that a succinct network structure is Preface IX obtained.
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We explain the role of learning as extending the reach of the designer into unknown environments, and we show how that role constrains agent design, favoring explicit knowl- edge representation and reasoning. Ant colony optimization ACO , a new meta-heuristic method based on the observation of real ant colony activities and behaviors, offers a new way to solve many complicated optimization problems.
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