| Budget Amount *help |
¥18,330,000 (Direct Cost: ¥14,100,000、Indirect Cost: ¥4,230,000)
Fiscal Year 2020: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2019: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
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| Outline of Final Research Achievements |
Recent developments in computer/AI and machine technology have led to promising applications of multi-agent systems consisting of multiple intelligent agents (e.g., self-driving robots) that make decisions autonomously and cooperate/coordinate with each other. Because agents are often software programs running on computers and/or controlling machines, their replacement, renewal, and periodic inspections are mandatory to maintain the sustainability and robustness of the system. However, there is a temporary but significant loss of performance that occurs when they are stopped for these purposes. To mitigate this, we proposed a negotiation method in which agents delegate tasks, especially important ones, to others. We also pursued a learning method that builds organization and division of labor among agents in a bottom-up manner to increase overall efficiency. We believe that our results have received academic recognition, including presentations at top-level conferences in this field.
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