In the previous post, we examined how agents make decisions in two-agent games, focusing on the Nash Equilibrium. In this post, we will explore how a group of agents make decisions, delving into the realm of social choice theory. A classic example of social choice theory is voting, where the challenge is to combine individual preferences to derive a social outcome. This post discusses voting mechanism in group decision making in Lecture 10 of the SC4003 course in NTU. Let’s begin!

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The allocation of scarce resources, such as physical objects or computing resources, among multiple agents is a crucial aspect of multi-agent systems. When resources are abundant or uncontested, allocation is straightforward. However, in reality, we often face scarcity and competition, which is where auction mechanisms come into play. Once considered rare, auctions have become a common method for resource allocation in various domains. We will cover the auction mechanism in Lecture 9 in the SC4003 course at NTU. Get ready!

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String matching is a fundamental problem in computer science, which involves finding the first occurrence of a given pattern in a text. This problem arises in many applications, such as searching for a character string in a text, finding a pattern in DNA sequences, decoding graphical or audio data, or searching for a sublist in linked lists. In this post, we will explore three different approaches to solving the string matching problem: a straightforward solution, the Rabin-Karp Algorithm, and the Boyer-Moore Algorithm. Also, this post is a lecture notes of the SC2001 course in NTU, covering common string matching algorithms.

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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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