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Toward Self-Referential Autonomous Learning of Object and Situation Models.
Cognitive Computation ( IF 5.4 ) Pub Date : 2016-04-27 , DOI: 10.1007/s12559-016-9407-7
Florian Damerow 1 , Andreas Knoblauch 2, 3 , Ursula Körner 3 , Julian Eggert 3 , Edgar Körner 3
Affiliation  

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach.

中文翻译:

面向对象和情况模型的自引用自主学习。

当前大多数的场景理解方法都缺乏将对象和情况模型适应人类系统设计人员无法预期的行为需求的能力。在这里,我们对自参考自主学习的系统体系结构进行了详细描述,该体系结构可在操作过程中优化对象和情况模型以优化行为。这包括针对预期和实际行动结果之间的不匹配而触发的情况和行为的层次模型的结构学习。除了提出架构概念之外,我们还描述了在模拟交通场景中系统的首次实现,以证明我们方法的可行性。
更新日期:2016-04-27
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