| Texto completo | |
| Autor(es): |
Ferreira, Leonardo Anjoletto
;
Bianchi, Reinaldo A. C.
;
Santos, Paulo E.
;
Lopez de Mantaras, Ramon
;
Mouhoub, M
;
Sadaoui, S
;
Mohamed, OA
;
Ali, M
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | RECENT TRENDS AND FUTURE TECHNOLOGY IN APPLIED INTELLIGENCE, IEA/AIE 2018; v. 10868, p. 13-pg., 2018-01-01. |
| Resumo | |
Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named Online ASP for MDP (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a changing environment. oASP(MDP) updates previously obtained policies, learnt by means of Reinforcement Learning (RL), using rules that represent the domain changes observed by the agent. These rules represent a set of domain constraints that are processed as ASP programs reducing the search space. Results show that oASP(MDP) is capable of finding solutions for problems in non-stationary domains without interfering with the action-value function approximation process. (AU) | |
| Processo FAPESP: | 16/18792-9 - Descrição, representação e solução de jogos espaciais |
| Beneficiário: | Paulo Eduardo Santos |
| Modalidade de apoio: | Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE |