The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available.
The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields.
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Agents and multi-agent systems are related to a modern software paradigm which has long been recognized as a promising technology for constructing autonomous, complex and intelligent systems. The topics covered in this volume include agent-oriented software engineering, agent co-operation, co-ordination, negotiation, organization and communication, distributed problem solving, multi-agent communities, rational and clustering agents, learning paradigms, agent cognitive models, and heterogenous multi-agent environments.
The volume highlights new trends and challenges in agent and multi-agent research and includes 30 papers classified in five specific topics: Modeling and logic agents, Knowledge based agent systems, Cognitive and cooperative multi-agent systems, Agent-based Modeling and Simulation, and Learning Paradigms and Applications: Agent-based Approach. The published papers have been presented at the 8th KES Conference on Agent and Multi-Agent Systems – Technologies and Applications (KES-AMSTA 2014) held in Chania on the island of Crete in Greece in June 2014.
The presented results will be of value to the research community working in the fields of artificial intelligence, collective computational intelligence, robotics, dialogue systems and, in particular, agent and multi-agent systems, technologies and applications.
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Buchbeschreibung Springer-Verlag Gmbh Jun 2010, 2010. Taschenbuch. Buchzustand: Neu. Neuware - Presented experiments show that usage ofevolutionary approach to feature - duction is justi ed.Feature selection as well as construction gives goodresults. It is noticeable that attribute construction s best results assign higher classi - tion accuracy than feature selection alone.That is why, carrying out selection before construction to decrease searchingspace isagoodsolution. Because of indeterministicbehavior of neuralnetworks,it was di cultto - ducefeaturesetincaseofusingthemto evaluatecandidateresults.Forexample, aneuralnetworklearntverywellondatathatwasdescribedbyfullattribute set, but when thisset was decreased it had huge problems to do this duringrequired number ofepochs.That suggests that usingC4.5 ismuchmore preferred. Numerous experiments havebeen performed and observed.Analysis ofabove results allowsto put the hypothesisthat it is worth to use Construction module as the feature set reduction. But experiments show that Constructormodule does not work sowell whenitusesthe whole initial set offeatures the search space istoo large.Soit is worth to use rstly Selectorand nextConstructor. The second important issue isthatConstructor destructs the semanticmeaning of the features.New constructed features are notunderstandableforusers.In some real-liveproblems measuring offeature values isquite expensive, forsuch problems selector seems to be more suitable because itdiminishes a number of realfeatures.To constructonefeaturesa number ofreal(measured)featurescan be required. Obtainedresults haveencouragedus to extendour system,especiallythe c- structormodule.Weplan to developenlarged set offunctionsFwhich allowsto use the system with data containingdi erenttype offeatures,not only nume- cal. Such system will be veri ed usingagreater number ofbenchmark data sets as well as real data. Acknowledgments. This work ispartially nanced fromthe Ministryof S- ence and Higher Education Republic of Polandresources in 2008 2010 years as a Poland Singapore joint research project 65/N-SINGAPORE/2007/0. 443 pp. Englisch. Artikel-Nr. 9783642135408