Encyclopedia of Artificial Intelligence最新文献

{"title":"Emerging Applications in Immersive Technologies","authors":"D. Davis, P. Chapman","doi":"10.4018/978-1-59904-849-9.CH082","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH082","url":null,"abstract":"The world of Virtual Environments and Immersive \u0000Technologies (Sutherland, 1965) (Kalawsky, 1993) \u0000are evolving quite rapidly. As the range and complexity \u0000of applications increases, so does the requirement \u0000for intelligent interaction. The now relatively simple \u0000environments of the OZ project (Bates, Loyall & Reilly, \u00001992) have been superseded by Virtual Theatres (Doyle \u0000& Hayes-Roth, 1997) (Giannachi, 2004), Tactical Combat \u0000Air (Jones, Tambe, Laird & Rosenbloom, 1993) \u0000training prototypes and Air Flight Control Simulators \u0000(Wangermann & Stengel, 1998). \u0000This article presents a brief summary of present \u0000and future technologies and emerging applications \u0000that require the use of AI expertise in the area of \u0000immersive technologies and virtual environments. \u0000The applications are placed within a context of prior \u0000research projects.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-Based Systems","authors":"A. Hopgood","doi":"10.4018/978-1-59904-849-9.CH146","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH146","url":null,"abstract":"The tools of artificial intelligence (AI) can be divided into two broad types: knowledge-based systems (KBSs) and computational intelligence (CI). KBSs use explicit representations of knowledge in the form of words and symbols. This explicit representation makes the knowledge more easily read and understood by a human than the numerically derived implicit models in computational intelligence. KBSs include techniques such as rule-based, modelbased, and case-based reasoning. They were among the first forms of investigation into AI and remain a major theme. Early research focused on specialist applications in areas such as chemistry, medicine, and computer hardware. These early successes generated great optimism in AI, but more broad-based representations of human intelligence have remained difficult to achieve.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114926876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EC Techniques in the Structural Concrete Field","authors":"J. L. P. Ordóñez, B. González-Fonteboa, F. M. Abella","doi":"10.4018/978-1-59904-849-9.CH080","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH080","url":null,"abstract":"Throughout the last decades, one of society’s concerns has been the development of new tools to optimize every aspect of daily life. One of the mechanisms that can be applied to this effect is what is nowadays called Artificial Intelligence (AI). This branch of science enables the design of intelligent systems, meaning that they display features that can be associated to human intelligence, search methods being one of the most remarkable. Amongst these, Evolutionary Computation (EC) stands out. This technique is based on the modelling of certain traits of nature, especially the capacity shown by living beings to adapt to their environment, using as a starting point Darwin’s Theory of Evolution following the principle of natural selection (Darwin, 1859). These models search for solutions in an automatized way. As a result, a series of search techniques which solve problems in an automatized and parallel way has arisen. The most successful amongst these are Genetic Algorithms (GA) and, more recently, Genetic Programming (GP). The main difference between them is rooted on the way solutions are coded, which implies certain changes in their processing, even though the operation in both systems is similar. Like most disciplines, the field of Civil Engineering is no stranger to optimization methods, which are applied especially to construction, maintenance or rehabilitation processes (Arciszewski and De Jong, 2001) (Shaw, Miles and Gray, 2003) (Kicinger, Arciszewski and De Jong, 2005). For instance, in Structural Engineering in general and in Structural Concrete in particular, there are a number of problems which are solved simultaneously through theoretical studies, based on physical models, and experimental benchmarks which sanction and adjust the former, where a large amount of factors intervene. In these cases, techniques based on Evolutionary Computation are capable of optimizing constructive processes while accounting for structural safety levels. In this way, for each particular case, the type of materials, their amount, their usage, etc. can be determined, leading to an optimal development of the structure and thus minimizing manufacturing costs (Rabunal , Varela, Dorado, Gonzalez and Martinez, 2005).","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116945808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Constraint Reasoning","authors":"M. Silaghi, M. Yokoo","doi":"10.4018/978-1-59904-849-9.CH077","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH077","url":null,"abstract":"This tutorial aims on the one hand side to provide an introduction into distributed constraint reasoning and on the other side to highlight the current research trends in the area. The tutorial draws from earlier tutorials by Makoto Yokoo (CP98), Jorg Denzinger (IJCAI01) and Yokoo, Denzinger and Marius Silaghi (IJCAI03, AAMAS04, IJCAI05) and from the PhD dissertations of Silaghi and Adrian Petcu. The previous tutorials were created as half-day tutorials and this was also the planned time frame for this tutorial. Consequently, we had the hard decisions to make which of the concepts to present and where to put foci. We decided to focus the introduction part on asynchronous methods and to highlight new approaches to constraint optimization and the whole topic of semi-cooperative agents with its connections to issues like privacy and reigning in the competitiveness of agents. To make up for this rather subjective selection, we include a structured bibliography of works on distributed knowledge-based search and distributed constraint reasoning with some notations of where to place the particular works.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"389 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124799842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic Approximation Monte Carlo for MLP Learning","authors":"F. Liang","doi":"10.4018/978-1-59904-849-9.CH217","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH217","url":null,"abstract":"Over the past several decades, multilayer perceptrons (MLPs) have achieved increased popularity among scientists, engineers, and other professionals as tools for knowledge representation. Unfortunately, there is no a universal architecture which is suitable for all problems. Even with the correct architecture, frustrating problems of connection weights training still remain due to the rugged nature of the energy landscape of MLPs. The energy function often refers to the sum-of-square error function for conventional MLPs and the negative logposterior density function for Bayesian MLPs. This article presents a Monte Carlo method that can be used for MLP learning. The main focus is on how to apply the method to train connection weights for MLPs. How to apply the method to choose the optimal architecture and to make predictions for future values will also be discussed, but within the Bayesian framework.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124983936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Management Systems Procedural Development","authors":"Javier Andrade Garda, Santiago Rodríguez Yáñez, M. Seoane, S. Suárez","doi":"10.4018/978-1-59904-849-9.CH144","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH144","url":null,"abstract":"The success of the organisations is increasingly dependant on the knowledge they have, to the detriment of other traditionally decisive factors as the work or the capital (Tissen, 2000). This situation has led the organisations to pay special attention to this new intangible item, so numerous efforts are being done in order to conserve and institutionalise it. The Knowledge Management (KM) is a recent discipline replying this increasing interest; however, and despite its importance, this discipline is currently in an immature stage, as none of the multiple existing proposals for the development of Knowledge Management Systems (KMS) achieve enough detail for perform such complex task. In order to palliate the previous situation, this work presents a methodological framework for the explicit management of the knowledge. The study has a formal basis for achieving an increased level of detail, as all the conceptually elements needed for understanding and representing the knowledge of any domain are identified. The requested descriptive character is achieved when basing the process on these elements and, in this way, the development of the systems could be guided more effectively.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125930625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovering Mappings Between Ontologies","authors":"V. Sorathia, Anutosh Maitra","doi":"10.4018/978-1-59904-849-9.CH075","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH075","url":null,"abstract":"Knowledge Representation is important part of AI. The purpose is to reveal best possible representation of the Universe of Discourse (UoD) by capturing entities, concepts and relations among them. With increased understanding of various scientific and technological disciplines, it is possible to derive rules that governs the behaviour and outcome of the entities in the UoD. In certain cases, it is not possible to establish any explicit rule, yet through experience or observation, some experts can define rules from their tacit knowledge in specific domain. Knowledge representation techniques are focused on techniques that allows externalization of implicit and explicit knowledge of expert(s) with a goal of reuse in absence of physical presence of such expertise. To ease this task, two parallel dimensions have developed over period of time. One dimension is focused on investigating more efficient methods that best suit the knowledge representation requirement resulting in theories and tools that allows capturing the domain knowledge (Brachman & Levesque, 2004). Another development has taken place in harmonization of tools and techniques that allows standard based representation of knowledge (Davies, Studer, & Warren, 2006). Various languages are proposed for representation of the knowledge. Reasoning and classification algorithms are also realized. As an outcome of standardization process, standards like DAML-OIL (Horrocks & PatelSchneider, 2001), RDF (Manola & Miller, 2004) and OWL(Antoniou & Harmelen, 2004) are introduced. Capturing the benefit of both developments, the tooling is also came in to existence that allows creation of knowledgebase. As a result of these developments, the amount of publicly shared knowledge is continuously increasing. At the time of this writing, a search engine like Swoogle (Ding et al., 2004)-developed to index publicly available Ontologies, is handling over 2,173,724 semantic web documents containing 431,467,096 triples. While the developments are yielding positive results by such a huge amount of knowledge available for reuse, it have become difficult to select and reuse required knowledge from this vast pool. The concepts and their relations that are important to the given problem could have already been defined in multiple Ontologies with different perspectives with specific level of details. It is very likely that to get complete representation of the knowledge, multiple Ontologies must be utilized. This requirement has introduced a new discipline within the domain of knowledge representation that is focused on investigation of techniques and tools that allows integration of multiple shared Ontologies.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123696530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary Approaches for ANNs Design","authors":"A. Azzini, A. Tettamanzi","doi":"10.4018/978-1-59904-849-9.CH088","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH088","url":null,"abstract":"Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neu-rons which process information using a connectionist approach.ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A par-ticular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologically-inspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs.Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006).The aim of this article is to present the main evolu-tionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116454923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer Vision for Wave Flume Experiments","authors":"Óscar Ibáñez, J. Rabuñal","doi":"10.4018/978-1-59904-849-9.CH059","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH059","url":null,"abstract":"During the past several decades, a number of attempts have been made to contain oil slicks (or any surface contaminants) in the open sea by means of a floating barrier. Many of those attempts were not very successful especially in the presence of waves and currents. The relative capabilities of these booms have not been properly quantified for lack of standard analysis or testing procedure (Hudon, 1992). In this regard, more analysis and experimental programs to identify important boom effectiveness parameters are needed. To achieve the desirable performance of floating booms in the open sea, it is necessary to investigate the static and dynamic responses of individual boom sections under the action of waves; this kind of test is usually carried out in a wave flume, where open sea conditions can be reproduced at a scale. Traditional methods use capacitance or conductivity gauges (Hughes, 1993) to measure the waves. One of these gauges only provides the measurement at one point; further, it isn’t able to detect the interphase between two or more fluids, such as water and a hydrocarbon. An additional drawback of conventional wave gauges is their cost. Other experiments such as velocity measurements, sand concentration measurements, bed level measurements, breakwater’s behaviour, etc... and the set of traditional methods or instruments used in those experiments which goes from EMF, ADV for velocity measurements to pressure sensors, capacity wires, acoustic sensors, echo soundings for measuring wave height and sand concentration, are common used in wave flume experiments. All instruments have an associate error (Van Rijn, Grasmeijer & Ruessink, 2000), and an associate cost (most of them are too expensive for a lot of laboratories that can not afford pay those amount of money), certain limitations and some of them need a large term of calibration. This paper presents another possibility for wave flume experiments, computer vision, which used a cheap and affordable technology (common video cameras and pc’s), it is calibrated automatically (once we have developed the calibration task), is a non-intrusive technology and its potential uses could takes up all kind experiments developed in wave flumes. Are artificial vision’s programmers who can give computer vision systems all possibilities inside the visual field of a video camera. Most experiments conducted in wave flumes and new ones can be carried out programming computer vision systems. In fact, in this paper, a new kind of wave flume experiment is presented, a kind of experiment that without artificial vision technology it couldn’t be done.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122336170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prototype Based Classification in Bioinformatics","authors":"Frank-Michael Schleif, T. Villmann, B. Hammer","doi":"10.4018/978-1-59904-849-9.CH196","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH196","url":null,"abstract":"INTRODUCTION Bioinformatics has become an important tool to support clinical and biological research and the analysis of functional data, is a common task in bioinformatics (Schleif, 2006). Gene analysis in form of micro array analysis (Schena, 1995) and protein analysis (Twyman, 2004) are the most important fields leading to multiple sub omics-disciplines like pharmacogenomics, glycoproteomics or metabolomics. Measurements of such studies are high dimensional functional data with few samples for specific problems (Pusch, 2005). This leads to new challenges in the data analysis. Spectra of mass spectrometric measurements are such functional data requiring an appropriate analysis (Schleif, 2006). Here we focus on the determination of classification models for such data. In general, the spectra are transformed into a vector space followed by training a classifier (Haykin, 1999). Hereby the functional nature of the data is typically lost. We present a method which takes this specific data aspects into account. A wavelet encoding (Mallat, 1999) is applied onto the spectral data leading to a compact functional representation. Subsequently the Supervised Neural Gas classifier (Hammer, 2005) is applied, capable to handle functional metrics as introduced by Lee & Verleysen (Lee, 2005). This allows the classifier to utilize the functional nature of the data in the modelling process. The presented method is applied to clinical proteome data showing good results and can be used as a bioinformatics method for biomarker discovery.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}