入学面接ではどんな質問がされますか？

談笑で和む
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軍訓の試験（特に近年の入試問題）について、ご記憶にあるお兄さんまたはお姉さんがいらっしゃいましたら、ぜひ教えていただけないでしょうか？心から感謝いたします。

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自己紹介（英語）

Hello, my name is [Your Name]. I am currently pursuing a degree in [Your Field of Study, e.g., Computer Science] and have a keen interest in Artificial Intelligence (AI) and Computer Science (CS). My academic background includes [briefly mention relevant courses, projects, or achievements, e.g., “studying machine learning algorithms and data structures”], and I am particularly fascinated by how AI intersects with CS to solve complex problems, such as [mention a specific topic, e.g., “natural language processing or autonomous systems”].

In my free time, I enjoy [hobbies, e.g., “reading technical papers, coding, or analyzing datasets”], and I am eager to contribute to discussions or projects that bridge theory with practical applications. I am also proficient in [mention languages/tools, e.g., “Python, Java, or TensorFlow”], and I believe collaboration and clear communication are key to innovation.

Answer to a Professional Question (English)
Q: How would you describe the differences and connections between AI and Computer Science (CS)?

A:
Artificial Intelligence (AI) and Computer Science (CS) are deeply interconnected but serve distinct roles in technology and innovation.

Differences:

  • Scope: CS is the broader field encompassing hardware, software, algorithms, networks, and theoretical foundations like data structures or cryptography. AI, however, is a specialized subset of CS focused on enabling machines to perform tasks requiring human-like intelligence, such as learning, reasoning, or perception.
  • Goal: CS aims to design and optimize computational systems, while AI targets creating systems that can adapt, learn, or make decisions autonomously (e.g., through machine learning or neural networks).

Connections:

  1. Foundational Tools: AI relies heavily on CS principles like algorithms (e.g., search, optimization), data structures (e.g., graphs for pathfinding in robotics), and programming paradigms (e.g., object-oriented design for software agents).
  2. Mathematical Frameworks: CS provides the statistical models (e.g., probability, linear algebra) and computational theory (e.g., complexity analysis) that underpin AI techniques like deep learning or reinforcement learning.
  3. Hardware Advancements: CS drives the development of hardware (e.g., GPUs for parallel processing) that accelerates AI workloads, while AI inspires new hardware designs (e.g., neuromorphic chips).
  4. Problem-Solving: AI often leverages CS techniques to solve domain-specific challenges—e.g., using natural language processing (NLP) algorithms (a CS subfield) to build chatbots or translation systems.

Example: A self-driving car combines CS components (e.g., sensor fusion algorithms, embedded systems) with AI modules (e.g., computer vision for object detection, reinforcement learning for decision-making). Without CS, AI lacks the infrastructure to deploy; without AI, CS remains static and limited to predefined tasks.

Professional Question (Chinese) – 红军协同对抗蓝军问题
问题背景（示例参考链接中的红蓝军对抗场景）：
假设红军部队（Red Team）需要协同作战以击败蓝军（Blue Team），其中红军有以下特点：

  • 单位类型：步兵（速度 v_r, 攻击力 a_r）、坦克（速度 v_t, 攻击力 a_t）、飞机（速度 v_a, 攻击力 a_a）。
  • 蓝军：单一类型（假设为坦克，速度 v_b, 攻击力 a_b）。
  • 地图：二维平面，红军初始位置为 (x_r, y_r)，蓝军初始位置为 (x_b, y_b)。
  • 规则：
    1. 所有单位每回合移动一次（速度为单位/回合）。
    2. 攻击范围：步兵/飞机可直线攻击（无视距离），坦克需在 d 距离内攻击。
    3. 协同机制：红军步兵+飞机可以形成“空地协同”优化攻击路径（飞机提供侦察，步兵清除地面障碍）。
    4. 能量/资源：每回合消耗 e 单位能量，
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ありがとうございます！お手伝いできて嬉しいです :smiling_face_with_three_hearts: