Abstract 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 …

Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial
environment offer challenging research opportunities. In this article, we introduce periodic …

Multi-agent systems in complex, real-time domains require agents to act effectively both
autonomously and as part of a team. This dissertation addresses multi-agent systems …

This paper presents layered learning, a hierarchical machine learning paradigm. Layered
learning applies to tasks for which learning a direct mapping from inputs to outputs is …

This is a comprehensive volume on robot teams that will be the standard reference on multi-
robot systems. The volume provides not only the essentials of multi-agent robotics theory but …

This thesis describes the incremental development and main features of a synthetic multi-
agent system called UvA Trilearn 2001. UvA Trilearn 2001 is a robotic soccer simulation …

We describe an effort to train a RoboCup soccer-playing agent playing in the Simulation
League using casebased reasoning. The agent learns (builds a case base) by observing the …

The CMUnited-99 simulator team became the 1999 RoboCup simulator league champion
by winning all 8 of its games, outscoring opponents by a combined score of 110-0 …

In order for robotic systems to be successful in domains with other agents possibly
interfering with the accomplishing of goals, the agents must be able to adapt to the …

We present an alternative to standard genetic programming (GP) that applies layered
learning techniques to decompose a problem. GP is applied to subproblems sequentially …