Name
Title | ||
|---|---|---|
An Improved Internal Model for Swarm Formation and Adaptive Swarm Behavior Acquisition |
Abstract | ||
|---|---|---|
To form a swarm and acquire swarm behaviors adaptive to the environment, we proposed a neuro-fuzzy learning system as a common internal model of each individual recently. The proposed swarm behavior learning system showed its efficient accomplishment in the simulation experiments of goal-exploration problems. However, the input information observed from the environment in our conventional methods was given by coordinate spaces (discrete or continuous) which were difficult to be obtained in the real world by the individuals. This paper intends to improve our previous neuro-fuzzy learning system to deal with the local-limited observation, i.e., usually being a Partially Observable Markov Decision Process (POMDP), by adopting eligibility traces and balancing trade-off between exploration and exploitation to the conventional learning algorithm. Simulations of goal-oriented problems for swarm learning were executed and the results showed the effectiveness of the improved learning system. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S0218126609005836 | JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS |
Keywords | Field | DocType |
Neuro-fuzzy system,reinforcement learning,swarm behavior | Swarm behavior,Swarm behaviour,Computer science,Partially observable Markov decision process,Multi-swarm optimization,Artificial intelligence,Machine learning,Internal model,Swarm robotics,Reinforcement learning | Journal |
Volume | Issue | ISSN |
18 | 8 | 0218-1266 |
Citations | PageRank | References |
3 | 0.49 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Kuremoto | 1 | 196 | 27.73 |
| Yuki Yamano | 2 | 3 | 0.49 |
| Masanao Obayashi | 3 | 198 | 26.10 |
| Kunikazu Kobayashi | 4 | 173 | 21.96 |