<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process</p></abstract>

In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

A strategy learning model for autonomous agents based on classification

Department of Computer Science AGH University of Science and Technology, al. Mickiewicza 30, 30-057 Kraków

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"A strategy learning model for autonomous agents based on classification"}

In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in...

A strategy learning model for autonomous agents based on classification

Article Category: Special Section Title: Agents in Intelligent Computing and Simulation Systems, Editors: Aleksander Byrski, Marek Kisiel-Dorohinicki, Grzegorz Dobrowolski

Data publikacji: 30 wrz 2015

Zakres stron: 471 - 482

Otrzymano: 14 lip 2014

DOI: https://doi.org/10.1515/amcs-2015-0035

© by Bartłomiej Śnieżyński

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.