Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.

Modelagem do consumo de energia de redes de sensores sem fio usando SystemC-AMS e stateflow

This work aims at modeling power consumption at the nodes of a Wireless Sensor Network (WSN). For doing so, a finite state machine was implemented by means of SystemC-AMS and Stateflow modeling and simulation tools. In order to achieve this goal, communication data in a WSN were collected. Based on the collected data, a simulation environment for power consumption characterization, which aimed at describing the network operation, was developed. Other than performing power consumption simulation, this environment also takes into account a discharging model as to analyze the battery charge level at any given moment. Such analysis result in a graph illustrating the battery voltage variations as well as its state of charge (SOC). Finally, a case study of the WSN power consumption aims to analyze the acquisition mode and network data communication. With this analysis, it is possible make adjustments in node-sensors to reduce the total power consumption of the network.

Automatic Categorization for Improving Spanish into Spanish Sign Language Machine Translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82.

Subsystem Testing of Message Guided State Machines Using a Simulator Framework

Simulaattorit ovat yksinkertaistettuja malleja tietyistä järjestelmän osioista. Niitä käytetään mallintamaan testattavan osion ympärillä olevien muiden osioiden ulkoista toimintaa, jotta testattavalle osiolle saadaan oikeanlainen toimintaympäristö aikaiseksi. Tilakoneita käytetään mallintamaan ohjelmistojen tai niiden osien toimintaa. Sanomaohjatuissa tilakoneissa tilojen vaihdot perustuvat saapuviin sanomiin. Tässä työssä esitellään erään ohjelmiston alijärjestelmän testaamisessa käytettävä arkkitehtuuri, joka perustuu suurelta osin simulaattoreiden käyttöön muiden alijärjestelmien mallintamisessa. Testattava ohjelmisto koostuu enimmäkseen tilakoneista, jotka vaihtavat keskenään sanomia ja ohjaavat näin toistensa tilasiirtymiä. Työn testausympäristö on suunniteltu juuri tämänkaltaisen ohjelmiston testaamiseen. Työssä esiteltävää testausympäristöä myöskin käytettiin useamman kuukauden ajan ja se todettiin toimivaksi. Joitakin testausympäristön käyttöohjeita, käyttökokemuksia sekä siihen liittyviä parannusehdotuksia käydään läpi työn loppuosassa. Erityisesti havaittiin miten tärkeää on testata implementaatiota jo luokka tasolla ennen alijärjestelmä tason testaukseen siirtymistä sekä päädyttiin siihen, että suunnitteluvaiheen pitäisi olla lähemmin liitoksissa alijärjestelmätestaukseen.

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Design And Development Of A Named Entity Based Question Answering System For Malayalam Language

This is a Named Entity Based Question Answering System for Malayalam Language. Although a vast amount of information is available today in digital form, no effective information access mechanism exists to provide humans with convenient information access. Information Retrieval and Question Answering systems are the two mechanisms available now for information access. Information systems typically return a long list of documents in response to a user’s query which are to be skimmed by the user to determine whether they contain an answer. But a Question Answering System allows the user to state his/her information need as a natural language question and receives most appropriate answer in a word or a sentence or a paragraph. This system is based on Named Entity Tagging and Question Classification. Document tagging extracts useful information from the documents which will be used in finding the answer to the question. Question Classification extracts useful information from the question to determine the type of the question and the way in which the question is to be answered. Various Machine Learning methods are used to tag the documents. Rule-Based Approach is used for Question Classification. Malayalam belongs to the Dravidian family of languages and is one of the four major languages of this family. It is one of the 22 Scheduled Languages of India with official language status in the state of Kerala. It is spoken by 40 million people. Malayalam is a morphologically rich agglutinative language and relatively of free word order. Also Malayalam has a productive morphology that allows the creation of complex words which are often highly ambiguous. Document tagging tools such as Parts-of-Speech Tagger, Phrase Chunker, Named Entity Tagger, and Compound Word Splitter are developed as a part of this research work. No such tools were available for Malayalam language. Finite State Transducer, High Order Conditional Random Field, Artificial Immunity System Principles, and Support Vector Machines are the techniques used for the design of these document preprocessing tools. This research work describes how the Named Entity is used to represent the documents. Single sentence questions are used to test the system. Overall Precision and Recall obtained are 88.5% and 85.9% respectively. This work can be extended in several directions. The coverage of non-factoid questions can be increased and also it can be extended to include open domain applications. Reference Resolution and Word Sense Disambiguation techniques are suggested as the future enhancements

Switching curve control of functional electrical stimulation assisted rowing exercise in paraplegia

An indoor rowing machine has been modified for functional electrical stimulation (FES) assisted rowing exercise in paraplegia. To perform the rowing manoeuvre successfully, however, the voluntarily controlled upper body movements must be co-ordinated with the movements of the electrically stimulated paralysed legs. To achieve such co-ordination, an automatic FES controller was developed that employs two levels of hierarchy. At the upper level, a finite state controller identifies the state or phase of the rowing cycle and activates the appropriate lower-level controller, in which electrical stimulation to the paralysed leg muscles is applied with reference to switching curves representing the desired seat velocity as a function of the seat position. In a pilot study, the hierarchical control of FES rowing was shown to be intuitive, reliable and easy to use. Compared with open-loop control of stimulation, all three variants of the closed-loop switching curve controllers used less muscle stimulation per rowing cycle (73% of the open-loop control on average). Further, the closed-loop controller that used switching curves derived from normal rowing kinematics used the lowest muscle stimulation (65% of the open-loop control) and was the most convenient to use for the client.

Stochastic stability for Markovian jump linear systems associated with a finite number of jump times

This paper deals with a stochastic stability concept for discrete-time Markovian jump linear systems. The random jump parameter is associated to changes between the system operation modes due to failures or repairs, which can be well described by an underlying finite-state Markov chain. In the model studied, a fixed number of failures or repairs is allowed, after which, the system is brought to a halt for maintenance or for replacement. The usual concepts of stochastic stability are related to pure infinite horizon problems, and are not appropriate in this scenario. A new stability concept is introduced, named stochastic tau-stability that is tailored to the present setting. Necessary and sufficient conditions to ensure the stochastic tau-stability are provided, and the almost sure stability concept associated with this class of processes is also addressed. The paper also develops equivalences among second order concepts that parallels the results for infinite horizon problems. (C) 2003 Elsevier B.V. All rights reserved.

A new class of symbolic abstract neural nets

Starting from the way the inter-cellular communication takes place by means of protein channels and also from the standard knowledge about neuron functioning, we propose a computing model called a tissue P system, which processes symbols in a multiset rewriting sense, in a net of cells similar to a neural net. Each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses (?excitations?) to the neighboring cells. Such cell nets are shown to be rather powerful: they can simulate a Turing machine even when using a small number of cells, each of them having a small number of states. Moreover, in the case when each cell works in the maximal manner and it can excite all the cells to which it can send impulses, then one can easily solve the Hamiltonian Path Problem in linear time. A new characterization of the Parikh images of ET0L languages are also obtained in this framework.

Algebraic Theory of Minimal Nondeterministic Finite Automata with Applications

Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA's behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.

Lexicon-grammar of Russian verbal idioms

Dissertação de Mestrado, Ciências da Linguagem, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2016

Dynamic Human Robot Interaction Framework Using Deep Learning and Robot Operating System (ROS): a practical approach

Trying to explain to a robot what to do is a difficult undertaking, and only specific types of people have been able to do so far, such as programmers or operators who have learned how to use controllers to communicate with a robot. My internship's goal was to create and develop a framework that would make that easier. The system uses deep learning techniques to recognize a set of hand gestures, both static and dynamic. Then, based on the gesture, it sends a command to a robot. To be as generic as feasible, the communication is implemented using Robot Operating System (ROS). Furthermore, users can add new recognizable gestures and link them to new robot actions; a finite state automaton enforces the users' input verification and correct action sequence. Finally, the users can create and utilize a macro to describe a sequence of actions performable by a robot.

A presta????o pelo setor p??blico n??o estatal dos servi??os de sa??de p??blica: an??lise de hip??teses e potencialidades

O trabalho analisa a potencialidade do desenvolvimento de alian??as entre o p??blico e o privado na gest??o p??blica municipal brasileira da sa??de. A relev??ncia da quest??o pauta-se na transfer??ncia de responsabilidade da presta????o de servi??os de sa??de para os munic??pios, posterior ?? promulga????o da Constitui????o Federal, aliada ?? limita????o da capacidade de gest??o dos mesmos. As an??lises aqui tratadas referem-se ??s alian??as previstas no arcabou??o legal brasileiro, estabelecidas entre o ente p??blico e o terceiro setor. Essas alian??as s??o introduzidas pela reforma do aparelho do Estado, em 1995, no ??mbito da qual se utilizou a estrat??gia de publiciza????o que tratou do fortalecimento dessa alian??a entre o Estado e o Terceiro Setor. A partir dos modelos poss??veis de parcerias com o terceiro setor, este estudo apresenta uma an??lise do modelo das organiza????es sociais (OS), trazendo ?? luz estrat??gias e desafios para sua implementa????o.

A CMOS micro power switched-capacitor DC-DC step-up converter for indoor light energy harvesting applications

This paper presents a micro power light energy harvesting system for indoor environments. Light energy is collected by amorphous silicon photovoltaic (a-Si:H PV) cells, processed by a switched capacitor (SC) voltage doubler circuit with maximum power point tracking (MPPT), and finally stored in a large capacitor. The MPPT fractional open circuit voltage (V-OC) technique is implemented by an asynchronous state machine (ASM) that creates and dynamically adjusts the clock frequency of the step-up SC circuit, matching the input impedance of the SC circuit to the maximum power point condition of the PV cells. The ASM has a separate local power supply to make it robust against load variations. In order to reduce the area occupied by the SC circuit, while maintaining an acceptable efficiency value, the SC circuit uses MOSFET capacitors with a charge sharing scheme for the bottom plate parasitic capacitors. The circuit occupies an area of 0.31 mm(2) in a 130 nm CMOS technology. The system was designed in order to work under realistic indoor light intensities. Experimental results show that the proposed system, using PV cells with an area of 14 cm(2), is capable of starting-up from a 0 V condition, with an irradiance of only 0.32 W/m(2). After starting-up, the system requires an irradiance of only 0.18 W/m(2) (18 mu W/cm(2)) to remain operating. The ASM circuit can operate correctly using a local power supply voltage of 453 mV, dissipating only 0.085 mu W. These values are, to the best of the authors' knowledge, the lowest reported in the literature. The maximum efficiency of the SC converter is 70.3 % for an input power of 48 mu W, which is comparable with reported values from circuits operating at similar power levels.

Dynamic programming for a Markov-switching jump–diffusion

We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump–diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman’s optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton–Jacobi–Belman equation, which turns out to be a partial integro-differential equation due to the extra terms arising from the Lévy process and the Markov process. As an application of our results, we study a finite horizon consumption– investment problem for a jump–diffusion financial market consisting of one risk-free asset and one risky asset whose coefficients are assumed to depend on the state of a continuous time finite state Markov process. We provide a detailed study of the optimal strategies for this problem, for the economically relevant families of power utilities and logarithmic utilities.