A Reinforcement Learning Based Method for Optimizing the Process of Decision Making in Fire Brigade Agents

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

abdolmaleki, a (Autor) Outra; movahedi, m (Autor) Outra; salehi, s (Autor) Outra; lau, n (Autor) Outra; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE

Páginas: 340-351

15th Portuguese Conference on Artificial Intelligence (EPIA 2011)

Lisbon, PORTUGAL, OCT 10-13, 2011

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 4 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-002-VYG

DOI: 10.1007/978-3-642-24769-9_25

Abstract (EN): Decision making in complex, multi agent and dynamic environments such as disaster spaces is a challenging problem in Artificial Intelligence. Uncertainty, noisy input data and stochastic behavior which are common characteristics of such environment makes real time decision making more complicated. In this paper an approach to solve the bottleneck of dynamicity and variety of conditions in such situations based on reinforcement learning is presented. This method is applied to RoboCup Rescue Simulation Fire brigade agent's decision making process and it learned a good strategy to save civilians and city from fire. The utilized method increases the speed of learning and it has very low memory usage. The effectiveness of the proposed method is shown through simulation results.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12