Encyclopedia of Artificial Intelligence最新文献

{"title":"Symbol Grounding Problem","authors":"Angelo C. Loula, J. Queiroz","doi":"10.4018/978-1-59904-849-9.CH226","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH226","url":null,"abstract":"INTRODUCTION The topic of representation acquisition, manipulation and use has been a major trend in Artificial Intelligence since its beginning and persists as an important matter in current research. Particularly, due to initial focus on development of symbolic systems, this topic is usually related to research in symbol grounding by artificial intelligent systems. Symbolic systems, as proposed by Newell & Simon (1976), are characterized as a high-level cognition system in which symbols are seen as \" [lying] at the root of intelligent action \" (Newell and Simon, 1976, p.83). Moreover, they stated the Physical Symbol Systems Hypothesis (PSSH), making the strong claim that \" a physical symbol system has the necessary and sufficient means for general intelligent action \" (p.87). This hypothesis, therefore, sets equivalence between symbol systems and intelligent action, in such a way that every intelligent action would be originated in a symbol system and every symbol system is capable of intelligent action. The symbol system described by Newell and Simon (1976) is seen as a computer program capable of manipulating entities called symbols, 'physi-cal patterns' combined in expressions, which can be created, modified or destroyed by syntactic processes. Two main capabilities of symbol systems were said to provide the system with the properties of closure and completeness, and so the system itself could be built upon symbols alone (Newell & Simon, 1976). These capabilities were designation – expressions designate objects – and interpretation – expressions could be processed by the system. The question was, and much of the criticism about symbol systems came from it, how these systems, built upon and manipulating just symbols, could designate something outside its domain. Symbol systems lack 'intentionality', stated John Searle (1980), in an important essay in which he described a widely known mental experiment (Gedan-kenexperiment), the 'Chinese Room Argument'. In this experiment, Searle places himself in a room where he is given correlation rules that permits him to determine answers in Chinese to question also in Chinese given to him, although Searle as the interpreter knows no Chinese. To an outside observer (who understands Chinese), the man in this room understands Chinese quite well, even though he is actually manipulating non-interpreted symbols using formal rules. For an outside observer the symbols in the questions and answers do represent something, but for the man in the room the symbols lack intentionality. The man in the room acts like a symbol system, …","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"2 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":"114255549","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":"Workflow Management Based on Mobile Agent Technology","authors":"Marina Flores-Badillo, E. López-Mellado","doi":"10.4018/978-1-59904-849-9.CH237","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH237","url":null,"abstract":"Nowadays Information Systems (IS) are designed for individual task execution control allowing coordinating, monitoring, and supporting the logistical aspects of a business process, in other words, the IS has to manage the flow of work through the organization. The WorkFlow Management represents a critical issue for achieving enterprise competitiveness among organizations. Many companies have realized that the business processes (BP) within their organizations, and between the companies and their partners have not been clearly described and there are not enough techniques and methods to automate the processes. The Workflow Management Coalition (WFMC) states that workflow (WF) is concerned with the automation of procedures where documents, information, or tasks are passed to the participants according to a defined set of rules to achieve, or contribute to, an overall business goal (WfMC, 1999). Another definition of WF can be found in (Rusinkiewicz & Seth, 1994) where workflows are activities involving the coordinated execution of multiple tasks performed by different processing entities (persons or machines). A task or process involves a piece of work and a process entity which executes the work. Workflow Management (WFM) is a fast evolving technology which is increasingly being exploited by businesses in a variety of industries. Its primary characteristic is the automation of processes involving combinations of human and machine-based activities (Aalst & Hee, 2002), (Aalst, 1998). A Workflow Management System (WFMS) provides procedural automation of a business process by management of the sequence of work activities and the invocation of appropriate human and/or IT resources associated with the various activity steps. Although the most prevalent use of WFMS is within the office environment in staff intensive operations such as insurance, banking, legal and general administrations, etc, it is also applicable to some classes of industrial and manufacturing applications (WfMC, 1995). WFMS needs to integrate other technologies such that agent technology, which provides flexible, distributed, and intelligent solutions for business process management. This work presents a methodology for mobile agentbased WFMS development. The proposed methodology consists of a modular and gradual specification of the system where a mobile agent guides the process through organizational units and executes different tasks. Several mobile agents evolve through the system executing concurrently their assigned task.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"36 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":"114298926","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":"Analytics for Noisy Unstructured Text A Data II","authors":"L. V. Subramaniam, Shourya Roy","doi":"10.4018/9781599048499.ch016","DOIUrl":"https://doi.org/10.4018/9781599048499.ch016","url":null,"abstract":"The importance of text mining applications is growing proportionally with the exponential growth of electronic text. Along with the growth of internet many other sources of electronic text have become really popular. With increasing penetration of internet, many forms of communication and interaction such as email, chat, newsgroups, blogs, discussion groups, scraps etc. have become increasingly popular. These generate huge amount of noisy text data everyday. Apart from these the other big contributors in the pool of electronic text documents are call centres and customer relationship management organizations in the form of call logs, call transcriptions, problem tickets, complaint emails etc., electronic text generated by Optical Character Recognition (OCR) process from hand written and printed documents and mobile text such as Short Message Service (SMS). Though the nature of each of these documents is different but there is a common thread between all of these—presence of noise. An example of information extraction is the extraction of instances of corporate mergers, more formally MergerBetween(company1,company2,date), from an online news sentence such as: “Yesterday, New-York based Foo Inc. announced their acquisition of Bar Corp.” Opinion(product1,good), from a blog post such as: “I absolutely liked the texture of SheetK quilts.” At superficial level, there are two ways for information extraction from noisy text. The first one is cleaning text by removing noise and then applying existing state of the art techniques for information extraction. There in lies the importance of techniques for automatically correcting noisy text. In this chapter, first we will review some work in the area of noisy text correction. The second approach is to devise extraction techniques which are robust with respect to noise. Later in this chapter, we will see how the task of information extraction is affected by noise.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 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":"117009075","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":"Learning Nash Equilibria in Non-Cooperative Games","authors":"A. Garro","doi":"10.4018/978-1-59904-849-9.CH150","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH150","url":null,"abstract":"Game Theory (Von Neumann & Morgenstern, 1944) is a branch of applied mathematics and economics that studies situations (games) where self-interested interacting players act for maximizing their returns; therefore, the return of each player depends on his behaviour and on the behaviours of the other players. Game Theory, which plays an important role in the social and political sciences, has recently drawn attention in new academic fields which go from algorithmic mechanism design to cybernetics. However, a fundamental problem to solve for effectively applying Game Theory in real word applications is the definition of well-founded solution concepts of a game and the design of efficient algorithms for their computation. A widely accepted solution concept of a game in which any cooperation among the players must be selfenforcing (non-cooperative game) is represented by the Nash Equilibrium. In particular, a Nash Equilibrium is a set of strategies, one for each player of the game, such that no player can benefit by changing his strategy unilaterally, i.e. while the other players keep their strategies unchanged (Nash, 1951). The problem of computing Nash Equilibria in non-cooperative games is considered one of the most important open problem in Complexity Theory (Papadimitriou, 2001). Daskalakis, Goldbergy, and Papadimitriou (2005), showed that the problem of computing a Nash equilibrium in a game with four or more players is complete for the complexity class PPAD-Polynomial Parity Argument Directed version (Papadimitriou, 1991), moreover, Chen and Deng extended this result for 2-player games (Chen & Deng, 2005). However, even in the two players case, the best algorithm known has an exponential worst-case running time (Savani & von Stengel, 2004); furthermore, if the computation of equilibria with simple additional properties is required, the problem immediately becomes NP-hard (Bonifaci, Di Iorio, & Laura, 2005) (Conitzer & Sandholm, 2003) (Gilboa & Zemel, 1989) (Gottlob, Greco, & Scarcello, 2003). Motivated by these results, recent studies have dealt with the problem of efficiently computing Nash Equilibria by exploiting approaches based on the concepts of learning and evolution (Fudenberg & Levine, 1998) (Maynard Smith, 1982). In these approaches the Nash Equilibria of a game are not statically computed but are the result of the evolution of a system composed by agents playing the game. In particular, each agent after different rounds will learn to play a strategy that, under the hypothesis of agent’s rationality, will be one of the Nash equilibria of the game (Benaim & Hirsch, 1999) (Carmel & Markovitch, 1996). This article presents SALENE, a Multi-Agent System (MAS) for learning Nash Equilibria in noncooperative games, which is based on the above mentioned concepts.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 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":"129496149","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":"Genetic Algorithm Applications to Optimization Modeling","authors":"Pi-Sheng Deng","doi":"10.4018/978-1-59904-849-9.CH111","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH111","url":null,"abstract":"Genetic algorithms (GAs) are stochastic search techniques based on the concepts of natural population genetics for exploring a huge solution space in identifying optimal or near optimal solutions (Davis, 1991)(Holland, 1992)(Reeves & Rowe, 2003), and are more likely able to avoid the local optima problem than traditional gradient based hill-climbing optimization techniques when solving complex problems. In essence, GAs are a type of reinforcement learning technique (Grefenstette, 1993), which are able to improve solutions gradually on the basis of the previous solutions. GAs are characterized by their abilities to combine candidate solutions to exploit efficiently a promising area in the solution space while stochastically exploring new search regions with expected improved performance. Many successful applications of this technique are frequently reported across various kinds of industries and businesses, including function optimization (Ballester & Carter, 2004)(Richter & Paxton, 2005), financial risk and portfolio management (Shin & Han, 1999), market trading (Kean, 1995), machine vision and pattern recognition (Vafaie & De Jong, 1998), document retrieval (Gordon, 1988), network topological design (Pierre & Legault, 1998)(Arabas & Kozdrowski, 2001), job shop scheduling (Özdamar, 1999), and optimization for operating system’s dynamic memory configuration (Del Rosso, 2006), among others. In this research we introduce the concept and components of GAs, and then apply the GA technique to the modeling of the batch selection problem of flexible manufacturing systems (FMSs). The model developed in this paper serves as the basis for the experiment in Deng (2007).","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 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":"129572746","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":"Intelligent Software Agents with Applications in Focus","authors":"Mario Jankovic-Romano, M. Stanković, Uros Krcadinac","doi":"10.4018/978-1-59904-849-9.CH140","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH140","url":null,"abstract":"Most people are familiar with the concept of agents in real life. There are stock-market agents, sports agents, real-estate agents, etc. Agents are used to filter and present information to consumers. Likewise, during the last couple of decades, people have developed software agents, that have the similar role. They behave intelligently, run on computers, and are autonomous, but are not human beings. Basically, an agent is a computer program that is capable of performing a flexible and independent action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents are capable of performing actions and making decisions without the guidance of a human. Software agents emerged in the IT because of the ever-growing need for information processing, and the problems concerning dealing and working with large quantities of data. Especially important is how agents act with other agents in the same environment, and the connections they form to find, refine and present the information in a best way. Agents certainly can do tasks better if they perform together, and that is why the multi-agent systems were developed. The concept of an agent has become important in a diverse range of sub-disciplines of IT, including software engineering, networking, mobile systems, control systems, decision support, information recovery and management, e-commerce, and many others. Agents are now used in an increasingly wide number of applications — ranging from comparatively small systems such as web or e-mail filters to large, complex systems such as air-traffic control, that have a large dependency on fast and precise decision making. Undoubtedly, the main contribution to the field of intelligent software agents came from the field of artificial intelligence (AI). The main focus of AI is to build intelligent entities and if these entities sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, and Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that constantly drive forward the development of agents.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 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":"128354207","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":"State-of-the-Art on Video-Based Face Recognition","authors":"Yan Yan, Yujin Zhang","doi":"10.4018/978-1-59904-849-9.CH213","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH213","url":null,"abstract":"Recently,video-based face recognition has become one of the hottest topics in the domain of face recognition.How to fully utilize both spatial and temporal information in video to overcome the difficulties existing in the video-based face recognition,such as low resolution of face images in video,large variations of face scale,radical changes of illumination and pose as well as occasionally occlusion of different parts of faces,is the focus.The paper reviews most existing typical methods for video-based face recognition(especially for the last 5 years)and analyses their respective pros and cons.Two commonly used video face databases and some experimental results are given.The prospects for future development and suggestions for further research works are put forward in the end.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 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":"129259075","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":"Complex-Valued Neural Networks","authors":"T. Nitta","doi":"10.4018/978-1-59904-849-9.CH055","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH055","url":null,"abstract":"The usual real-valued artificial neural networks have been applied to various fields such as telecommunications, robotics, bioinformatics, image processing and speech recognition, in which complex numbers (two dimensions) are often used with the Fourier transformation. This indicates the usefulness of complex-valued neural networks whose input and output signals and parameters such as weights and thresholds are all complex numbers, which are an extension of the usual real-valued neural networks. In addition, in the human brain, an action potential may have different pulse patterns, and the distance between pulses may be different. This suggests that it is appropriate to introduce complex numbers representing phase and amplitude into neural networks. Aizenberg, Ivaskiv, Pospelov and Hudiakov (1971) (former Soviet Union) proposed a complex-valued neuron model for the first time, and although it was only available in Russian literature, their work can now be read in English (Aizenberg, Aizenberg & Vandewalle, 2000). Prior to that time, most researchers other than Russians had assumed that the first persons to propose a complex-valued neuron were Widrow, McCool and Ball (1975). Interest in the field of neural networks started to grow around 1990, and various types of complex-valued neural network models were subsequently proposed. Since then, their characteristics have been researched, making it possible to solve some problems which could not be solved with the real-valued neuron, and to solve many complicated problems more simply and efficiently.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"28 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":"123466621","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":"Genetic Fuzzy Systems Applied to Ports and Coasts Engineering","authors":"Óscar Ibáñez, A. Castro","doi":"10.4018/978-1-59904-849-9.CH113","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH113","url":null,"abstract":"Fuzzy Logic (FL) and fuzzy sets in a wide interpretation of FL (in terms in which fuzzy logic is coextensive with the theory of fuzzy sets, that is, classes of objects in which the transition from membership to non membership is gradual rather than abrupt) have placed modelling into a new and broader perspective by providing innovative tools to cope with complex and ill-defined systems. The area of fuzzy sets has emerged following some pioneering works of Zadeh (Zadeh, 1965 and 1973) where the first fundamentals of fuzzy systems were established. Rule based systems have been successfully used to model human problem-solving activity and adaptive behaviour. The conventional approaches to knowledge representation are based on bivalent logic. A serious shortcoming of such approaches is their inability to come to grips with the issue of uncertainty and imprecision. As a consequence, the conventional approaches do not provide an adequate model for modes of reasoning. Unfortunately, all commonsense reasoning falls into this category. The application of FL to rule based systems leads us to fuzzy systems. The main role of fuzzy sets is representing Knowledge about the problem or to model the interactions and relationships among the system variables. There are two essential advantages for the design of rule-based systems with fuzzy sets and logic:","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"29 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":"116247001","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":"AVI of Surface Flaws on Manufactures II","authors":"G. Fornarelli, A. Giaquinto","doi":"10.4018/978-1-59904-849-9.CH032","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH032","url":null,"abstract":"INTRODUCTION Automatic visual inspection takes a relevant place in defect detection of industrial production. In this field a fundamental role is played by methods for the detection of superficial anomalies on manufactures. In particular, several systems have been proposed in order to reduce the burden of human operators, avoiding the drawbacks due to the subjectivity of judgement criteria (Kwak, Ventura & Tofang-Sazi 2000, Patil, Biradar & Jadhav 2005). Proposed solutions are required to be able to handle and process a large amount of data. For this reason, neural networks-based methods have been suggested for their ability to deal with a wide spread of data in many cases these methods must satisfy time constrains of industrial processes, because the inclusion of the diagnosis inside the production process is needed. To this purpose, architectures, based on Cellular Neural Networks (CNNs), revealed successful in the field of real time defect detection, due to the fact that these networks guarantee a hardware implementation On the basis of these considerations, a method to identify superficial damages and anomalies in manufactures has been given in (Fornarelli & Giaquinto 2007). This method is aimed at the implementation by means of an architecture entirely formed by Cellular Neural Networks, whose synthesis is illustrated in the present work. The suggested solution reveals effective for the detection of defects, as shown by two test cases carried out on an injection pump and a sample textile.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"26 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":"121618366","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}