Adaptive Learning Model for Predicting Negotiation Behaviors through Hybrid K-means Clustering, Linear Vector Quantization and 2-Tuple Fuzzy Linguistic Model-Reference-Cited by-同舟云学术

Adaptive Learning Model for Predicting Negotiation Behaviors through Hybrid K-means Clustering, Linear Vector Quantization and 2-Tuple Fuzzy Linguistic Model

Published:2014 Issue: Volume:36 Page:285-292
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Agarwal Siddhartha,Saferpour Hamid R.,Dagli Cihan H.

Publisher

Elsevier BV

Subject

General Engineering

Reference27 articles.

1. Agarwal, S., Wang, R., & Dagli, C., Executable Architectures using Cuckoo Search Optimization coupled with OPM and CPN-A module: A new Meta-Architecture Model for FILA-SoS, In Complex Systems Design & Management, 2014. Springer Berlin Heidelberg.

2. Agarwal, S., Pape, L., Ergin, N., & Dagli, C.H. (2014)., Multi-agent Based Architecture for Acknowledged System of Systems, Procedia Computer Science, Volume 28, 2014, Pages 1-10, ISSN 1877-0509.

3. An, B., Lesser, V., & Sim, K.M. (2011). Strategic agents for multi-resource negotiation. Autonomous Agents and Multi-Agent Systems, 23(1), 114-153.

4. Baarslag, T., Hindriks, K., & Jonker, C. (2013). Acceptance conditions in automated negotiation. In Complex Automated Negotiations: Theories, Models, and Software Competitions (pp. 95-111). Springer Berlin Heidelberg.

5. Bi, X., & Xiao, J. (2012). Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation. Frontiers of Computer Science, 6(4), 442-461.

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