As we delve into the realm of multiagent decision making, game theory emerges as a powerful tool for understanding strategic interactions between self-interested agents. Born from the intersection of economics, mathematics, and computer science, game theory provides a robust framework for analyzing complex decision-making processes. In this post, we’ll embark on an exploration of the game theory foundations that underlie the intricate dance of cooperation and competition among self-interested agents. This journey will cover the essential concepts and theories that form the backbone of Lecture 8 in the SC4003 course at NTU. Buckle up, and let’s dive into the fascinating world of game theory!

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In the previous related posts, we discussed how agents can make decisions and act in an environment. However, we often have multiple agents working together in a multi-agent system. In this chapter, we will discuss why and how agents work together. Since agents are autonomous, they have to make decisions at run-time, and be capable of dynamic coordination. This coordination requires agents to share tasks and information. But, if agents are designed by different individuals, they may not have common goals. Therefore, we need to make a distinction between benevolent agents and self-interested agents. In this chapter, we will focus on benevolent agents, which are agents that are designed to work together and achieve a common goal. This post discuss cooperative agents in Lecture 7 of the SC4003 course in NTU.

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Recently, I am applying for a backend developer summer internship and I have been asked a few questions were quite interesting. This post is a collection of backend interview questions which I have encountered. Hope it can help you when you are finding yourself in the same situation 🌟. Since I am applying positions based in China, I will discuss the questions both in Chinese and English.

最近，我正在寻找后端开发的暑期实习，被问了一些有趣的问题。这篇博客是我遇到的问题的汇总，希望能帮助到需要的人。因为我寻找的是大陆的岗位，所以在本文中会同时使用英文和中文。📚

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Agent architecture is a software design for an intelligent agent. It is a concrete implementation of the abstract agent structure, which consists of perception, state, decision, and action. An agent architecture defines key data structures, operations on data structures, and control flow between operations. It is a crucial component of an AI system, as it directly influences the performance of the system. This post provides a comprehensive discussion of agent architecture in Lecture 5 of the SC4003 course in NTU.

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Agent decision making is a crucial aspect of artificial intelligence. It is the process by which an autonomous entity, such as a robot or a computer program, makes decisions based on its available knowledge and perception of the environment. In this post, we will explore the different types of decision making, including simple and complex decisions, as well as sequential decision making, where the agent’s utility depends on a sequence of decisions. We will also discuss the various techniques used in decision making. This post provides a comprehensive discussion of the agent decision making in Lecture 4 of the SC4003 course in NTU.

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