我们提供了机器人智能系统和控制（RISC）LAB MULTIAGEGGENT测试，用于在室外环境中的可靠搜索和救援和空中运输。该系统包括三个多陆无人机（无人机）的团队，能够在室外场中自主搜索，拾取和运输随机分布的物体。该方法涉及基于视觉的物体检测和定位，具有我们的新颖设计，基于GPS的UAV导航和下降区的物体的安全释放。我们的合作策略可确保无人机之间安全的空间分离，我们可以使用已启用的通信共识，防止下落区域的冲突。所有计算都在每个UAV上执行。我们描述了系统的完整软件和硬件架构，并使用全面的户外实验展示其可靠的性能，并通过将我们的结果与最近的一些类似的作品进行比较。

This thesis introduces quantum natural language processing (QNLP) models based on a simple yet powerful analogy between computational linguistics and quantum mechanics: grammar as entanglement. The grammatical structure of text and sentences connects the meaning of words in the same way that entanglement structure connects the states of quantum systems. Category theory allows to make this language-to-qubit analogy formal: it is a monoidal functor from grammar to vector spaces. We turn this abstract analogy into a concrete algorithm that translates the grammatical structure onto the architecture of parameterised quantum circuits. We then use a hybrid classical-quantum algorithm to train the model so that evaluating the circuits computes the meaning of sentences in data-driven tasks. The implementation of QNLP models motivated the development of DisCoPy (Distributional Compositional Python), the toolkit for applied category theory of which the first chapter gives a comprehensive overview. String diagrams are the core data structure of DisCoPy, they allow to reason about computation at a high level of abstraction. We show how they can encode both grammatical structures and quantum circuits, but also logical formulae, neural networks or arbitrary Python code. Monoidal functors allow to translate these abstract diagrams into concrete computation, interfacing with optimised task-specific libraries. The second chapter uses DisCopy to implement QNLP models as parameterised functors from grammar to quantum circuits. It gives a first proof-of-concept for the more general concept of functorial learning: generalising machine learning from functions to functors by learning from diagram-like data. In order to learn optimal functor parameters via gradient descent, we introduce the notion of diagrammatic differentiation: a graphical calculus for computing the gradients of parameterised diagrams.

建立公平的推荐系统是一个具有挑战性且至关重要的研究领域，因为它对社会产生了巨大影响。我们将两个普遍公认的公平概念的定义扩展到了推荐系统，即机会平等和均衡的赔率。这些公平措施确保同样对待“合格”（或“不合格”）候选人，无论其受保护的属性状况如何（例如性别或种族）。我们提出了可扩展的方法，以实现机会平等和在存在位置偏见的情况下排名均等的几率，这通常会困扰推荐系统产生的数据。我们的算法是模型不可知论，因为它们仅依赖于模型提供的最终分数，因此很容易适用于几乎所有Web尺度推荐系统。我们进行广泛的模拟以及现实世界实验，以显示我们方法的功效。