| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
We usually prove logical formulas by rewriting them step by step. Reduction machines have been proposed for functional programming languages to make an inference based on such a rewriting mechanism. However, it has not been efficient in distributed environment, since they rewrite a logical formula on a memory by using processors. A computer network has many switches, and transfers packets to their destinations. We propose to rewrite a formula in logic or algebra on distributed switches and state memories with higher-order meta-rules. Although such inference seems similar to one by a production rule in expert systems, it utilizes distributed working memories and self-optimizing properties in their inference with meta-rules. We show this mechanism is appropriate for weight-based learning for controlling its inference, and inter-subjective formalization for a sensor network in Empathic Computing.
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