Artificial intelligence combat method based on deep learning and robot system

A deep learning and matching technology, applied in the information field, can solve problems such as limited application of artificial intelligence and insufficient samples of combat cases, and achieve the effect of improving the effect and the ability of assisting decision-making in combat, and improving subjective initiative and intelligence

Active Publication Date: 2018-11-16
SUPERPOWER INNOVATION INTELLIGENT TECH DONGGUAN CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Based on this, it is necessary to address the defects or insufficiencies of the combat technology in the existing technology, and provide artificial intelligence combat methods and robot systems based on deep learning to solve the shortcomings of insufficient combat case samples and limited artificial intelligence applications in the prior art

Method used

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  • Artificial intelligence combat method based on deep learning and robot system
  • Artificial intelligence combat method based on deep learning and robot system
  • Artificial intelligence combat method based on deep learning and robot system

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0067] Embodiment 1 provides a method of combat, such as figure 1 As shown, the method includes step S110 to step S150.

[0068] Step S110 to Step S120: Generate combat case samples and combat deep learning models through self-learning. In the stage of combat speculation, the robot is allowed to understand the combat situation and combat intention, and to speculate on combat decision-making. This stage also corresponds to the self-study stage of the teaching method, because this stage is mainly the self-learning of the robot to generate combat case samples and combat deep learning models.

[0069] Sample generating step S110: generating a plurality of first combat case samples, the first combat case samples including combat situation, combat intention, and combat decision of the preset party. Preferably, when the number of the first combat case samples is large, the first combat case sample big data can be formed. Adding multiple first combat case samples to the first comba...

Embodiment 2

[0075] Embodiment 2 provides a preferred combat method, according to the combat method described in Embodiment 1,

[0076] Such as figure 2 As shown, the specific process generated in the sample generation step S110 includes:

[0077] Situation generating step S111: generating the combat situation in the first combat case sample according to the preset combat situation knowledge base. Preferably, the combat situation knowledge base is pre-built, and the combat situation knowledge base pre-stores the combat situation composition rule sub-knowledge base and the combat situation composition element sub-knowledge base. Combat situation composition rule sub-knowledge base includes combination rules of enemy attributes, enemy capabilities, enemy real-time status, our attributes, our capabilities, and our real-time status. Combat situation component sub-knowledge base includes attribute knowledge table, capability knowledge table, real-time status knowledge table, and other relate...

Embodiment 3

[0085] Embodiment 3 provides a kind of preferred combat method, according to the combat method described in embodiment 1 or embodiment 2, such as Figure 4 As shown, step S210 and step S220 are also included after step S150:

[0086] Steps S210 to S220 belong to the combat demonstration stage (essentially the stage of verifying the combat deep learning model). This stage also corresponds to the teaching stage of the teaching method, because this stage is mainly to test and improve the combat deep learning model generated in the self-study stage through real combat cases.

[0087] Model verification step S210: Screen out a plurality of real combat case samples whose matching degree between the combat result and the combat intention is greater than a preset threshold, and verify the combat deep learning model. The real combat case samples include combat situation, combat intention, combat decision, and combat result. Comprehensible samples of real combat cases are samples of com...

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Abstract

An artificial intelligence combat method based on deep learning and a robot system are provided; the method comprises the following steps: forming a plurality of first combat case samples; carrying out supervision-free training for a combat deep learning model according to the first combat case samples; selecting a plurality of first combat case samples that comply with preset conditions from theplurality of first combat case samples, and taking selected samples as a plurality of second combat case samples; carrying out supervised training for the combat deep learning model according to the second combat case samples. The method and system can automatically form combat case samples, thus solving the problems that effective deep learning and auxiliary decision cannot be carried out via a few combat case samples, improving the deep learning effect and the combat auxiliary decision capability, improving the deep learning model accuracy, and improving the combat robot subjective initiative and intelligence.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to an artificial intelligence combat method and robot system based on deep learning. Background technique [0002] Knowledge base is one of the important technologies in artificial intelligence, and knowledge base can assist humans in decision-making. [0003] Dialectics is divided into speculative stage, empirical stage, and the unified stage of speculative and empirical. The empirical stage is to test the results of the speculative stage. The unified stage of speculative and empirical is actually the stage of practicing the results of the speculative stage that have been tested and screened in the empirical stage. [0004] Commonly used teaching stages in teaching methods include self-study stage, teaching stage, and examination stage. [0005] In the process of realizing the present invention, the inventor found that there are at least the following problems in th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 朱定局
Owner SUPERPOWER INNOVATION INTELLIGENT TECH DONGGUAN CO LTD
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