Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel Q-learning algorithm that segmentalizes the agent environment and action. This algorithm is learned through interaction with an environment and provides deterministic space segmentation. The purposes of this study can be divided into two main groups: search domain reduction and heuristic space segmentation. In our method, the most activated space segment is divided into new two segments with the learning by a heuristic and recognizable method. Appropriate search domain reduction can minimize the learning time and enables us to recognize the evolutionary process. This segmentation method is also designed for social simulation models. Social space segmentation, such as language systems and culture, is revealed by multi-agent social simulation with our method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634129 | IJCNN |
Keywords | Field | DocType |
evolutionary computation,heuristic programming,learning (artificial intelligence),multi-agent systems,search problems,state-space methods,Q-learning,action space segmentation,agent environment,deterministic space segmentation,evolutionary process,heuristic space segmentation,language system,learning time minimization,multiagent social simulation,search domain reduction,social simulation model,social space segmentation,state space segmentation | Scale-space segmentation,Computer science,Segmentation-based object categorization,Multi-agent system,Image segmentation,Artificial intelligence,Artificial neural network,Heuristic,Pattern recognition,Segmentation,Q-learning,Algorithm,Machine learning | Conference |
ISSN | Citations | PageRank |
1098-7576 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Notsu | 1 | 146 | 42.93 |
| Hidetomo Ichihashi | 2 | 370 | 72.85 |
| Katsuhiro Honda | 3 | 289 | 63.11 |