**:** ..,

..
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**:**
**:**
**:** 2019
**:** ..

** :**
**:** 78
**:** .., .. // . 78. .: , 2019. .174-220. URL: https://doi.org/10.25728/ubs.2019.78.8

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** :** , , , ,

** (.):** learning, learning curve, technology, complex activity, control

**:** . . . , , / , / . . . . , . .

** (.):** The article continues the study of technology of complex activities [8]. The set of tasks of managing the development and mastering of new technologies of complex activity is considered. The problem of choosing standard solutions has been set and solved. In the framework of this task, it was shown that a uniform partition of the set of possible states of nature is asymptotically optimal from the point of view of minimizing the expected error, costs or / and entropy, and also maximizing the expected value of the level of learning and / or utility. For the tasks of resource distribution in aggregated technological networks, simple analytical algorithms for optimal resource allocation are proposed. The tasks of choosing the optimal strategy for switching from technology development to its productive use have been set and solved. For the case of a priori known nature characteristics, an optimal strategy was obtained and its properties were analytically investigated. For the case of unknown characteristics, a procedure is proposed that is optimal in the class of successive likelihood ratio rules. The results of simulation and analysis of approximations of the properties of the procedure are presented

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