Affordable Access

Publisher Website

A social approach for learning agents

Authors
Publisher
Elsevier Ltd
Volume
40
Issue
5
Identifiers
DOI: 10.1016/j.eswa.2012.10.008
Keywords
  • Multi-Agent Systems
  • Ensemble-Based Drift Detection
  • Social Network Theory
  • Negotiation Policies Learning
Disciplines
  • Computer Science

Abstract

Abstract In this paper, we propose a social approach for learning agents. In dynamic environments, smart agents should detect changes and adapt themselves, applying dynamic learning strategies and drift detection algorithms. Recent studies note that an ensemble of learners can be coordinated by simple protocols based on votes or weighted votes; however, they are not capable of determining the number of learners or the ensemble composition properly. Conversely, we show in this paper that Social Network Theory can provide the multi-agent learning community with sophisticated and well-founded reputation models that outperform well-known ensemble-based drift detection techniques, generating accurate and small ensembles of learning agents. Our approach is evaluated considering dynamic bilateral negotiation scenarios and benchmark databases, presenting statistically significant results that are better than those of other ensemble-based techniques.

There are no comments yet on this publication. Be the first to share your thoughts.