Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective

Title: Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective

Abstract: This article introduces the concept of learning in multiagent systems, focusing on game theory, utility theory, and machine learning. It explains how these techniques are used in the engineering of learning multiagent systems.

Introduction: Multiagent systems are composed of learning agents that use machine learning algorithms to increase their ability to match inputs to outputs. These systems often face challenges such as moving target functions and changing utility payoffs due to the learning dynamics of other agents. To understand and predict the behavior of these systems, researchers use game theory, utility theory, and machine learning.

Game Theory: Game theory is a branch of mathematics that models the decision-making process of rational humans. It focuses on simultaneous actions and the resulting utility payoffs for all participants. In multiagent systems, game theory helps predict the strategies that utility-maximizing agents might use.

Utility Theory: Utility theory is a branch of economics that deals with the principles of choice under uncertainty. It helps in understanding the preferences of agents and the value they place on different outcomes. In multiagent systems, utility theory is used to model the payoffs that agents receive based on their actions.

Machine Learning: Machine learning is a subset of artificial intelligence that focuses on developing algorithms that allow computers to improve their performance on a task without being explicitly programmed to do so. In multiagent systems, machine learning is used to enable agents to learn from their experiences and adapt to changing environments.

Learning in Multiagent Systems: The article discusses the challenges of learning in multiagent systems and introduces two approaches: CLRI theory and n-level learning agents. CLRI theory applies some findings from game theory to the engineering of multiagent systems, while n-level learning agents attempt to incorporate the learning dynamics of other agents.

Conclusion: The article summarizes the remaining challenges in the field of learning in multiagent systems and emphasizes the importance of understanding the complex dynamics of these systems. It encourages further research in this area to develop more effective learning algorithms and models for multiagent systems.

Link to Article: https://arxiv.org/abs/0308030v1 Authors: arXiv ID: 0308030v1