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Paper details
Number 3 - September 2015
Volume 25 - 2015
A strategy learning model for autonomous agents based on classification
Bartłomiej Śnieżyński
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.
Keywords
autonomous agents, strategy learning, supervised learning, classification, reinforcement learning