Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems

1. Front Matter

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2. Keynote Speeches

   1. Rough Set Approach to Knowledge Discovery about Preferences

      • Roman Słowiński

      Pages 1-21

   2. Toward a Self-referential Collective Intelligence Some Philosophical Background of the IEML Research Program

      • Pierre Lévy

      Pages 22-35

   3. A-Teams and Their Applications

      • Piotr Jȩdrzejowicz

      Pages 36-50

3. Collective Decision Making

   1. Local Search Algorithms for Core Checking in Hedonic Coalition Games

      • Helena Keinänen

      Pages 51-62

   2. Information Foraging Theory as a Form of Collective Intelligence for Social Search

      • Longo Luca, Barrett Stephen, Dondio Pierpaolo

      Pages 63-74

   3. SAM: Semantic Argumentation Based Model for Collaborative Knowledge Creation and Sharing System

      • Krissada Maleewong, Chutiporn Anutariya, Vilas Wuwongse

      Pages 75-86

   4. A Conception for Modification of Learning Scenario in an Intelligent E-learning System

      • Adrianna Kozierkiewicz-Hetmańska

      Pages 87-96

   5. Firefly Algorithm for Continuous Constrained Optimization Tasks

      • Szymon Łukasik, Sławomir Żak

      Pages 97-106

   6. Distributed Data Mining Methodology with Classification Model Example

      • Marcin Gorawski, Ewa Płuciennik-Psota

      Pages 107-117

   7. A Token-Based Mutual Exclusion Approach to Improve Collaboration in Distributed Environments

      • Mauricio Paletta, Pilar Herrero

      Pages 118-127

   8. Discovering Medical Knowledge from Data in Patients’ Files

      • Magdalena Szymkowiak, Beata Jankowska

      Pages 128-139

   9. Towards an Increase of Collective Intelligence within Organizations Using Trust and Reputation Models

      • Emil Scarlat, Iulia Maries

      Pages 140-151

   10. A New Ant Colony Optimization Algorithm with an Escape Mechanism for Scheduling Problems

       • Tsai-Duan Lin, Chuin-Chieh Hsu, Da-Ren Chen, Sheng-Yung Chiu

       Pages 152-162

   11. Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer

       • Huberto Ayanegui-Santiago

       Pages 163-173

   12. Semi-traces and Their Application in Concurrency Control Problem

       • Hoang Chi Thanh

       Pages 174-182

   13. Design of the Directory Facilitator Supporting Fault-Tolerance in Multi-OSGi Agent System

       • Sang-Hwan Ryu, Seung-Hyun Lee, Kyung-Soo Jang, Ho-Jin Shin, Dong-Ryeol Shin

       Pages 183-192

4. Multiagent Systems

   1. Agent-Based Provisioning of Group-Oriented Non-linear Telecommunication Services

      • Vedran Podobnik, Ana Petric, Krunoslav Trzec, Vedran Galetic, Gordan Jezic

      Pages 193-204

   2. A Multi-agent Model of Deceit and Trust in Intercultural Trade

      • Gert Jan Hofstede, Catholijn M. Jonker, Tim Verwaart

      Pages 205-216

   3. Implementation of Epistemic Operators for Model Checking Multi-agent Systems

      • Marina Bagić Babac, Marijan Kunštić

      Pages 217-228

Computational collective intelligence (CCI) is most often understood as a subfield of artificial intelligence (AI) dealing with soft computing methods that enable group decisions to be made or knowledge to be processed among autonomous units acting in distributed environments. The needs for CCI techniques and tools have grown signi- cantly recently as many information systems work in distributed environments and use distributed resources. Web-based systems, social networks and multi-agent systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions. Therefore, CCI is of great importance for today’s and future distributed systems. Methodological, theoretical and practical aspects of computational collective int- ligence, such as group decision making, collective action coordination, and knowledge integration, are considered as the form of intelligence that emerges from the collabo- tion and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc. , can support human and other collective intelligence and create new forms of CCI in natural and/or artificial s- tems.

• artificial intelligence
• collective intelligence
• consensus theory
• evolutionary computation
• fuzzy systems
• multiagent systems
• neural systems
• ontologies
• semantic web
• social networks

• Institute of Informatics, Wroclaw University of Technology, Wroclaw, Poland

  Ngoc Thanh Nguyen

• Centre for Complex Software Systems and Services, Swinburne University of Technology, Victoria, Australia

  Ryszard Kowalczyk

• Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.

  Shyi-Ming Chen

Policies and ethics