Name
Abstract | ||
|---|---|---|
This paper presents a multi-agent planning approach integrated with learning mechanism. This method involves in task allocation, path planning, avoiding conflicts, cooperation, parameter learning, pattern learning, etc. In addition, with this method a multi-agent Sokoban platform is defined. With some simulations on this platform, the advantages of multi-agent planning approach with learning mechanism are illustrated comparing with single-agent approach. Since the proposed method can improve the efficiency and capability of multi-agent planning, by referring to the results of this research, the proposed method will be adopted for multi-robot system in the future research. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ROBIO.2009.4913231 | ROBIO |
Keywords | Field | DocType |
multirobot system,pattern learning,task allocation,multi-agent planning approach,planning (artificial intelligence),multi-agent planning,multiagent sokoban platform,multi-robot systems,parameter learning,multi-agent systems,multiagent planning,single-agent approach,path planning,knowledge model,learning mechanism,multi-robot system,conflict avoidance,sokoban platform,multi-agent sokoban platform,multiagent systems,multi agent systems,control systems,resource management,planning,knowledge based systems | Motion planning,Resource management,Industrial engineering,Computer science,Knowledge-based systems,Multi-agent system,Parameter learning,Control engineering,Conflict avoidance,Artificial intelligence,Multi-agent planning,Control system | Conference |
ISBN | Citations | PageRank |
978-1-4244-2679-9 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhang | 1 | 422 | 100.57 |
| Liang Zheng | 2 | 0 | 0.34 |
| Haruki Ueno | 3 | 129 | 18.02 |