Szczegóły publikacji
Opis bibliograficzny
A strategy learning model for autonomous agents based on classification / Bartłomiej ŚNIEŻYŃSKI // International Journal of Applied Mathematics and Computer Science ; ISSN 1641-876X. — 2015 — vol. 25 no. 3, s. 471–482. — Bibliogr. s. 480–481
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 93977 |
|---|---|
| Data dodania do BaDAP | 2015-11-05 |
| Tekst źródłowy | URL |
| DOI | 10.1515/amcs-2015-0035 |
| Rok publikacji | 2015 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | International Journal of Applied Mathematics and Computer Science |
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.
