Name
Abstract | ||
|---|---|---|
We propose a novel method, a particle filter on episode, for decision makings of agents in the real world. This method is used for simulating behavioral experiments of rodents as a workable model, and for decision making of actual robots. Recent studies on neuroscience suggest that hippocampus and its surroundings in brains of mammals are related to solve navigation problems, which are also essential in robotics. The hippocampus also handle memories and some parts of a brain utilize them for decision. The particle filter gives a calculation model of decision making based on memories. In this paper, we have verified that this method learns two kinds of tasks that have been frequently examined in behavioral experiments of rodents. Though the tasks have been different in character from each other, the algorithm has been able to make an actual robot take appropriate behavior in the both tasks with an identical parameter set. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-48036-7_54 | INTELLIGENT AUTONOMOUS SYSTEMS 14 |
Keywords | Field | DocType |
Particle filter,Decision making,Learning,Episodic memory | Episodic memory,Computer science,Particle filter,Artificial intelligence,Robot,Machine learning,Robotics | Conference |
Volume | ISSN | Citations |
531 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryuichi Ueda | 1 | 0 | 0.68 |
| Kotaro Mizuta | 2 | 0 | 0.34 |
| Hiroshi Yamakawa | 3 | 3 | 4.58 |
| Hiroyuki Okada | 4 | 14 | 5.40 |