This book constitutes the refereed proceedings of the 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, held in Toronto, Canada, in May 2010. The 15 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on agents for data mining; data mining for agents; data mining in agents; and agent mining applications.
This book constitutes the refereed proceedings of the Second International Workshop on Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2007, held in St. Petersburg, Russia in June 2007. The 17 revised full papers and six revised short papers presented together with four invited lectures cover agent and data mining, agent competition and data mining, as well as text mining, semantic Web, and agents.
Data Mining and Multi agent Integration aims to re?ect state of the art research and development of agent mining interaction and integration (for short, agent min ing). The book was motivated by increasing interest and work in the agents data min ing, and vice versa. The interaction and integration comes about from the intrinsic challenges faced by agent technology and data mining respectively; for instance, multi agent systems face the problem of enhancing agent learning capability, and avoiding the uncertainty of self organization and intelligence emergence. Data min ing, if integrated into agent systems, can greatly enhance the learning skills of agents, and assist agents with predication of future states, thus initiating follow up action or intervention. The data mining community is now struggling with mining distributed, interactive and heterogeneous data sources. Agents can be used to man age such data sources for data access, monitoring, integration, and pattern merging from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an excellent opportunity to create innovative, dual agent mining interac tion and integration technology, tools and systems which will deliver results in one new technology.
This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.
Multi-agent system (MAS) is an expanding field in science and engineering. It merges classical fields like game theory with modern ones like machine learning and computer science. This book provides a succinct introduction to the subject, covering the theoretical fundamentals as well as the latter developments in a coherent and clear manner. The book is centred on practical applications rather than introductory topics. Although it occasionally makes reference to the concepts involved, it will do so primarily to clarify real-world applications. The inner chapters cover a wide spectrum of issues related to MAS uses, which include collision avoidance, automotive applications, evacuation simulation, emergence analyses, cooperative control, context awareness, data (image) mining, resilience enhancement and the management of a single-user multi-robot.
In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.
This volume contains the papers selected for presentation at the 2010 Inter- tionalConference onActiveMedia Technology(AMT2010),jointlyheldwiththe 2010 International Conference on Brain Informatics (BI 2010), at York Univ- sity, Toronto, Canada, during August 28-30, 2010. Organized by the Web Int- ligence Consortium (WIC) and IEEE Computational Intelligence Society Task Force on Brain Informatics (IEEE-CIS TF-BI), this conference was the sixth in the AMT series since its debut conference at Hong Kong Baptist University in 2001 (followed by AMT 2004 in Chongqing, China, AMT 2005 in Kagawa, Japan, AMT 2006 in Brisbane, Australia, AMT 2009 in Beijing, China). Active media technology (AMT) is a new area of research and development in intelligent information technology and computer science. It emphasizes the proactive, adaptive and seamless roles of interfaces and systems as well as new media in all aspects of digital life. Over the past few years, we have witnessed rapiddevelopmentsofAMT technologiesandapplicationsrangingfrombusiness and communication to entertainment and learning. Examples include Facebook, Twitter, Flickr, YouTube, Moodle, Club Penguinand GoogleLatitude. Such - velopmentshavegreatlychangedourlivesbyenhancingthewaywecommunicate and do business.
th Welcome to the proceedings of the 10 International Conference on Intelligent Virtual Agents (IVA), held 20-22 September, 2010 in Philadelphia, Pennsylvania, USA. Intelligent Virtual Agents are interactive characters that exhibit human-like qualities and communicate with humans or with each other using natural human modalities such as behavior, gesture, and speech. IVAs are capable of real-time perception, cognition, and action that allow them to participate in a dynamic physical and social environment. IVA 2010 is an interdisciplinary annual conference and the main forum for prese- ing research on modeling, developing, and evaluating Intelligent Virtual Agents with a focus on communicative abilities and social behavior. The development of IVAs - quires expertise in multimodal interaction and several AI fields such as cognitive modeling, planning, vision, and natural language processing. Computational models are typically based on experimental studies and theories of human-human and hum- robot interaction; conversely, IVA technology may provide interesting lessons for these fields. Visualizations of IVAs require computer graphics and animation te- niques, and in turn supply significant realism problem domains for these fields. The realization of engaging IVAs is a challenging task, so reusable modules and tools are of great value. The fields of application range from robot assistants, social simulation, and tutoring to games and artistic exploration. The enormous challenges and diversity of possible applications of IVAs have - sulted in an established annual conference.
Scientific Essay from the year 2014 in the subject Computer Science - Applied, grade: A, University of North Texas (Department of Computer Science), course: Distributed and Parallel Databases, language: English, abstract: With the increase in the usage of databases in various fields and domains, to overcome the challenges in a centralized data mining environment, more and more databases are distributed in networks. The objective of distributed data mining is to perform data mining operations based on the type and availability of distributed resources. To make a proper choice of a particular DDM system/model, the basic differences between each of them must be understood. This paper produces a survey of some of the DDM systems available. It mainly focusses on the homogeneous DDM models. It discusses methods based on semantic web and grid, multi-agent, mobile agent and i-Analyst. A hybrid method AGrIP is also discussed. A comparative analysis is made considering different key issues of DDM. Each method is described in detail by its method/algorithm.
Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference.
