From the Publisher:
This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence. The book provides detailed coverage of basic topics as well as several closely related ones and is suitable as a textbook. The book can be used for teaching as well as self-study, and it is designed to meet the needs of both researchers and practitioners. In view of the interdisciplinary nature of the field, it will be a useful reference not only for computer scientists and engineers, but for social scientists and management and organization scientists as well.

Multiagent Systems : A Modern Approach to Distributed Artificial Intelligence

https://people.dsv.su.se/~fk/id2010/articles/Jennings_1999.pdf

On agent-based software engineering

/pdf/automated-negotiation-prospects-methods-and-challenges-4yefoms8r2.pdf

Automated Negotiation: Prospects, Methods and Challenges

https://digital.csic.es/bitstream/10261/160955/1/RAS24%281998%29_159-82.pdf

Negotiation Decision Functions for Autonomous Agents

Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goals Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.

/pdf/multiagent-systems-a-survey-from-a-machine-learning-yps633tity.pdf

Jeffrey S. Rosenschein and Gilad Zlotkin

Papers

Rules of encounter