Abstract
Multi-Agent-Based Decision Support Systems (MADSS) have emerged as
powerful tools for facilitating decision-making in complex and dynamic environments.
This chapter provides an overview of MADSS, highlighting their fundamental
concepts, key components, and applications. MADSS leverage the principles of multi-agent systems, artificial intelligence, and decision support systems to enable
collaborative decision-making among multiple autonomous agents. The chapter begins
by introducing the concept of multi-agent systems, emphasizing the advantages they
offer in terms of adaptability, flexibility, and scalability. It then explores the integration
of decision support systems within this framework, enabling agents to make informed
decisions by analyzing vast amounts of data, evaluating various alternatives, and
considering multiple criteria. The architecture of MADSS is discussed, focusing on the
interactions among agents, the coordination mechanisms employed, and the
information exchange protocols utilized. Various agent types, such as user agents,
decision agents, and knowledge agents, are described, along with their roles and
responsibilities within the system. The chapter further explores the different approaches
and techniques used in MADSS, including rule-based systems, expert systems,
machine learning, and optimization algorithms. It highlights the importance of agent
learning and adaptation to improve decision-making capabilities over time. The
applications of MADSS across various domains are presented, including finance,
supply chain management, healthcare, and transportation. Case studies illustrate how
MADSS can enhance decision-making processes, improve efficiency, and optimize
resource allocation in complex real-world scenarios.
Lastly, the chapter discusses the challenges and future directions of MADSS. Issues
such as agent coordination, trust among agents, and handling uncertainty are addressed.
The potential of integrating emerging technologies like blockchain, the Internet of
Things (IoT), and big data analytics is also explored, envisioning more sophisticated
MADSS capable of handling larger-scale problems.