| Project/Area Number |
18560401
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
System engineering
|
|---|
| Research Institution | Yokohama National University |
|---|
Principal Investigator |
HAMAGAMI Tomoki Yokohama National University, Graduate school of Engineering, associate professor (30334204)
|
|---|
| Project Period (FY) |
2006 – 2007
|
| Project Status |
Completed (Fiscal Year 2007)
|
| Budget Amount *help |
¥2,980,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥180,000)
Fiscal Year 2007: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2006: ¥2,200,000 (Direct Cost: ¥2,200,000)
|
|---|
| Keywords | systems engineering / information system / artificial intelligence / intelligent robotics / intelligent machine / 知能ロボティックス |
|---|
| Research Abstract |
For creation of future social welfare infrastructure, fundamental studies of distributed intelligence, evaluations with simulation experiments, and feasibility studies of applications have been developed. The state of the art of this study is that the intelligent algorithm can develop dependable and low cost devices appropriate for the component of welfare and social systems. In order to realize these intelligent systems, this study proposes a new reinforcement learning algorithm, approaches with multi agent design, and implements of new distributed intelligent applications. The proposed methods and techniques enable agents to acquire autonomously adaptive behavior in several task according to environments, and to construct adaptive state space in spite of sensor limitations for efficient learning. Specifically the following main outcomes have been brought from this study.
(1) A new method of constructing effective state space with low dependence on sensor configurations is conducted. T
… More
he method consists of abstracting surrounding environment by rectangles, and generalization of state space by classifying with self organization map(SOM). Simulation experiments show that the state space that has been constructed can be reused for the agent with another sensor configuration.
(2) Q-learning with action values in complex numbers is proposed to overcome agent's learning under partially observable Markov decision processes environments. It is expected that the technique enables an agent to acquire context-dependent behaviors similar to complex-valued neural networks. The results of both experiments showed that there are possibilities for agents to learn behaviors under the POMDPs environments by using contexts.
(3) A multi agent based autonomous power distribution network restoration system by using a new genetic algorithm and contract network protocol under the distributed resources is proposed. The proposed system enables us to improve the restoration performance, and to reduce computational resources and costs. The simulation results show the proposed method achieves to improve the performance under the pure distributed environment. Less
|
