How to solve a classification problem using a cooperative tiling Multi-Agent System?

  title={How to solve a classification problem using a cooperative tiling Multi-Agent System?},
  author={Thibault Fourez and Nicolas Verstaevel and Fr{\'e}d{\'e}ric Migeon and Fr'ed'eric Schettini and Fr{\'e}d{\'e}ric Amblard},
  booktitle={Practical Applications of Agents and Multi-Agent Systems},
. Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems of local cooperation between agents. We present smapy , an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for… 

Figures and Tables from this paper



The AMAS theory for complex problem solving based on self-organizing cooperative agents

  • D. CaperaJ. GeorgéM. GleizesP. Glize
  • Computer Science
    WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003.
  • 2003
An approach for the design of complex adaptive systems, based on adaptive multi-agent systems and emergence, which gives local agent design criteria so as to enable the emergence of an organization within the system and thus, of the global function of the system.

Dynamic Filtering of Useless Data in an Adaptive Multi-Agent System: Evaluation in the Ambient Domain

An extended version of Amadeus taking account of the large number of devices that generally compose ambient systems and filtering useless data is proposed and a solution based on cooperative interactions between the different agents composing Amadeu is proposed.

Cooperative Neighborhood Learning: Application to Robotic Inverse Model

This paper proposes an endogenous self-learning strategy to improve learning performances and shows how the addition of artificial learning situations increases the performances of the learnt model and decreases the required labeled learning data.

AMAK - A Framework for Developing Robust and Open Adaptive Multi-agent Systems

AMAK, a framework developed in Java to facilitate the design and development of a multi-agent system, is proposed and the particularity of Adaptive Multi-Agent Systems is presented.

The Self-Adaptive Context Learning Pattern: Overview and Proposal

The pattern enabling the dynamic and interactive learning of the mapping between context and actions by the self-adaptive multi-agent systems is presented.

A decision-theoretic generalization of on-line learning and an application to boosting

The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.

Random Forests

Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.

Online Passive-Aggressive Algorithms

This work presents a unified view for online classification, regression, and uni-class problems, and proves worst case loss bounds for various algorithms for both the realizable case and the non-realizable case.

Stochastic gradient boosting