Propagation of Action Knowledge in Multi-Agent Systems

Abstract

Currently, an inportant topic in robotic reasearch systems is the design and development of learning Multi-Agent Systems (MAS). One major advantage of these systems is the fact that several agent work towards a common goal, having different specializations for specific subtasks. Especially learning MAS in cooperation with a human teacher seem to be a very promising approach for complex manipulation and production tasks. Since agents can join and leave the system at any time, it is important that knowledge acquired by single agents can be transferred or propagated between agents, to ensure that knowledge is not lost, if agents leave the system. Therefore, techniques will be presented to represent extentable action knowledge for task solutions in an agent's knowledge base and additionally, algorithms for propagating this knowledge between agents efficiently and with minimum communication effort.