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首页> 外文会议>IEEE International Conference on Cognitive Infocommunications >Denormalized quantum density operators for encoding semantic uncertainty in cognitive agents
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Denormalized quantum density operators for encoding semantic uncertainty in cognitive agents

机译：用于编码认知主体语义不确定性的非标准化量子密度算符

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The design of a cognitive agent requires a behaviour control of actions and observations for exploring an unknown world. Typically, observations are influenced by a certain degree of randomness which can be modeled as probabilities. In our scenario we let a mouse explore a maze with walls, boundaries and a random portal. All observations are stored and managed in a so-called inner stage. As a decision problem, we want to be able to plan actions and to predict their resulting observations. In our approach we develop models of the inner stage based on concepts of probabilistic databases and their mapping to denormalized density matrices which are known from quantum mechanics. Density matrices provide a compact representation of the powerful but unwieldy many-world-semantics. We show that density matrices make the many-world-semantics feasible and are well suited to model the inner stage. We propose algorithms for learning and predicting action results.
机译：认知主体的设计要求对行为和行为的行为进行控制，以探索未知的世界。通常，观察结果受一定程度的随机性影响，可以将其建模为概率。在我们的场景中，我们让鼠标探索具有墙壁，边界和随机入口的迷宫。所有观察值都在所谓的内部阶段进行存储和管理。作为决策问题，我们希望能够计划行动并预测其结果。在我们的方法中，我们基于概率数据库的概念开发内部阶段的模型，并将其映射到量子力学中已知的非规格化密度矩阵。密度矩阵提供了功能强大但笨拙的多世界语义的紧凑表示。我们证明了密度矩阵使多世界语义学可行，并且非常适合于对内部阶段进行建模。我们提出了用于学习和预测动作结果的算法。

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