| Project/Area Number |
18500130
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Toyohashi University of Technology |
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Principal Investigator |
NITTA Tsuneo Toyohashi University of Technology, Graduate School of Engineering, Professor (70314101)
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| Co-Investigator(Kenkyū-buntansha) |
KATSURADA Kouichi Toyohashi University of Technology, Graduate School of Engineering, lecturer (80324490)
IRIBE Yurie Toyohashi University of Technology, Information and Media Center, Assistant Professor (40397500)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥4,040,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥540,000)
Fiscal Year 2007: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2006: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Symbiosis of human and agents / Communication / Dialogue strategies / Word meaning acquisition / Learning biases / Reinforcement learning / 相互排他性バイアス / 形状類似バイアス |
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| Research Abstract |
The symbiotic society of human and agents that enables them real interaction in the near future is expected. To accelerate related studies, facilities of spoken dialogue interaction and development tools are very important. We have studied first: (a) word-meaning acquisition of image objects, (b) dialogue strategy acquisition for the agents.
In the word-meaning acquisition, we propose a method based on an Online-EM algorithm that enables agents to find out the features of objects represented by spoken words, however, the algorithm needs a lot of examples to determine the word-meaning, or correct distributions. To overcome the problem, we have applied two types of learning biases, shape bias and mutual exclusivity bias, observed in children's language development. Experimental results showed that the improved method with biases could efficiently acquire word meanings.
In the dialogue strategy acquisition, we propose a Q-learning based algorithm in which agents acquire different strategies according with their roles, asking or teaching, by estimating counterpart's comprehension level from its facial expression and utterance. Experimental results showed the effectiveness of both strategies of teaching and asking. We have also developed a tool with the abovementioned functionalities of word-meaning acquisition and dialogue strategy acquisition. The experimental results on cooperative works showed the effectiveness of acquired action strategies to complete tasks quickly.
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