Multi-heuristic and game approaches in search problems of the graph theory

We consider in this paper the adaptation of heuristics used for programming non-deterministic games to the problems of discrete optimization, in particular, some heuristic methods of decision-making in various discrete optimization problems. The object of each of these problems is programming anytime algorithms. Among the problems solved in this paper, there are the classical traveling salesman problem and some connected problems of minimization for nondeterministic finite automata. Considered methods for solving these problems are constructed on the basis of special combination of some heuristics, which belong to some different areas of the theory of artificial intelligence. More precisely, we shall use some modifications of unfinished branch-and-bound method; for the selecting immediate step using some heuristics, we apply dynamic risk functions; simultaneously for the selection of coefficients of the averaging-out, we also use genetic algorithms; and the reductive self-learning by the same genetic methods is also used for the start of unfinished branch-and-bound method again. This combination of heuristics represents a special approach to construction of anytime-algorithms for the discrete optimization problems. This approach can be considered as an alternative to application of methods of linear programming, and to methods of multi-agent optimization, and also to neural networks

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Aplicación de inteligencia artificial en videojuegos : uso de la variante del algoritmo Minimax poda alpha-beta para su desarrollo

Actualmente, los videojuegos forman parte del día a día de un gran porcentaje de personas, con independencia de su edad, sexo, etc. Estos videojuegos, cuya complejidad puede variar desde unas pocas líneas de código a proyectos de varios años que involucran un alto número de programadores y diseñadores no habrían tenido tal éxito de no haber sido por un factor determinante: el desarrollo de la inteligencia artificial en ellos. Por ello, en este proyecto se presentará brevemente la historia de la inteligencia artificial en los videojuegos desde sus comienzos, definiendo los hitos más relevantes y desarrollando un pequeño juego que sirve como ejemplo para definir los puntos importantes a la hora de desarrollar una inteligencia artificial en un videojuego. ABSTRACT Nowadays, video games take part of the routine of a wide range of people, regardless of their age, gender, etc. These video games, whose complexity can range from a few lines of code to a several years project involving a large number of programmers and designers would not have had such success if it was not for a determining factor: the development of artificial intelligence. Therefore, in this project it will be briefly presented the history of artificial intelligence in video games from the beginning, defining the most relevant milestones and developing a small game that serves as an example to explain the most important points to take into account when the artificial intelligence of a videogame is being developed

Resource Constrained Design of Artificial Neural Networks Using Comparator Neural Networks

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Aeronautics and Space Administration / NASA NCC 2-48

Learning to read aloud: A neural network approach using sparse distributed memory

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested

Pré-distorção neuronal analógica de amplificadores de potência

Mestrado em Engenharia Electrónica e TelecomunicaçõesAs especificações das redes de telecomunicações de quinta geração ultrapassam largamente as capacidades das técnicas mais modernas de linearização de amplificadores de potência como a pré-distorção digital. Por esta razão, esta tese propõe um método de linearização alternativo: um prédistorçor analógico, à banda base, constituído por uma rede neuronal artificial. A rede foi treinada usando três métodos distintos: avaliação de política através de TD(λ), otimização por estratégias de evolução como CMA-ES, e um algoritmo original de aproximações sucessivas. Apesar do TD(λ) não ter produzido resultados de simulação satisfatórios, os resultados dos outros dois métodos foram excelentes: um NMSE entre as funções de transferência pretendida e efetiva do amplificador pré-distorcido até -70 dB, e uma redução total das componentes de distorção do espetro de frequência de um sinal GSM de teste. Apesar das estratégias de evolução terem alcançado este nível de linearização após cerca de 4 horas de execução contínua, o algoritmo original consegue fazê-lo numa questão de segundos. Desta forma, esta tese abre caminho para que se cumpram as exigências das redes de nova geração.Fifth-generation telecommunications networks are expected to have technical requirements which far outpace the capabilities of modern power amplifier (PA) linearization techniques such as digital predistortion. For this reason, this thesis proposes an alternative linearization method: a base band analog predistorter consisting of an artificial neural network. The network was trained through three very distinct methods: policy evaluation using TD(λ), optimization using evolution strategies such as CMA-ES, and an original algorithm of successive approximations. While TD(λ) proved to be unsuccessful, the other two methods produced excellent simulation results: an NMSE between the target and the predistorted PA transfer functions up to -70 dB, and the complete elimination of distortion components in the frequency spectrum of a GSM test signal. While the evolution strategies achieved this level of linearization after about 4 hours of continuous work, the original algorithm consistently does so in a matter of seconds. In effect, this thesis outlines a way towards the meeting of the specifications of next-generation networks

Applications of nonlinear dynamics to information processing

The reported results are direct applications of nonlinear dynamics to information processing or are relevant for the applications. In the second chapter we describe a simple method for estimating the embedding dimension that can be used as a first step in constructing nonlinear models. The method for the reduction of measurement noise in chaotic systems that is presented in the third chapter is attractive in the cases where high accuracy is necessary. Next we propose how to overcome some problems encountered in constructing models of complex nonlinear systems. Finally, the behaviour of one-dimensional cellular automata useful for the detection of velocities of patterns is shown and explained in the last chapter. The method of estimating the embedding dimension is based on the idea that when the observed dynamical system is deterministic and smooth and the embedding dimension is correctly chosen, the relationship between the successive reconstructed state vectors should be described as a continuous mapping. To check if the given embedding dimension is a good one we search for pairs of state vectors whose distance is smaller than some number. For each pair we compute the distance between the successors of the elements of pairs and represent this distance graphically. When the embedding dimension is equal or larger than the minimum correct dimension, all distances are small in comparison to distances for incorrect dimensions. The method for noise reduction is developed assuming that the map of the system is known and the noise is bounded. The closer the initial condition is to the true state of the system, the longer the computed trajectory follows the observed trajectory. To reduce the uncertainty in knowing the given state we recursively search for the state for which the computed trajectory follows the observed trajectory as long as possible. The method is demonstrated on several twodimensional invertible and noninvertible chaotic maps. When the map is known exactly an arbitrary level of noise reduction can be achieved. With the increase of the complexity of a nonlinear system it is harder to construct its model. We propose to discover first how to construct a model of a similar but simple system. Discovered heuristics can be useful in modeling more complex systems. We demonstrate the approach by constructing a deterministic feed-forward neural network that can extract velocities of onedimensional patterns. Analysing simpler models we discovered how to estimate the necessary numbers of neurons; what are the useful ranges of the parameters of the network and what are the potential functional dependencies between the parameters. The class of one-dimensional cellular automata whose state is a function of both the previous state and a time-dependant input is described. As inputs we considered the sequences of binary strings that represent black-and-white objects moving in front of a white background. As outputs we considered the trajectory of the automaton. For some rules the automaton will evolve to the zero state for all velocities of the object except for the velocities in specific narrow range. The phenomenon is persistent even when a strong noise is present in input patterns but unreliable units of the automaton or having a more complex input break it down