In distributed arti cial intelligence systems it is important that the constituent intelligent systems communicate This may be a problem if the systems use di erent methods to represent uncertain in formation This paper presents a method that enables systems that use di erent uncertainty handling formalisms to qualitatively integrate their uncertain information and argues that this makes it pos...

Coordination, the process by which an agent reasons about its local actions and the (anticipated) actions of others to try and ensure the community acts in a coherent manner, is perhaps the key problem of the discipline of Distributed Artificial Intelligence (DAI). In order to make advances it is important that the theories and principles which guide this central activity are uncovered and anal...

Teaching Artificial Intelligence (AI) or multi-agent systems is a challenging task as algorithms are in question which are advantageous in highly complex and dynamic environments. Explaining multi-agent systems (MAS) in lectures requires interactive approaches accompanied by exercises. The key challenge in using practical exercises within lectures on MAS is to establish an environment for testi...

Over the last four decades, machine learning's primary interest has been single agent learning. In general, single agent learning involves improving the performance or increasing the knowledge of a single agent [5]. An improvement in performance or an increase in knowledge allows the agent to solve past problems with better quality or efficiency. An increase in knowledge may also allow the agen...

Agents are a central concept in Distributed Artificial Intelligence. What truly defines an agent and in what manner they are used is very broad. Distributed Problem Solving and Multiagent Systems are fields within Distributed Artificial Intelligence that use the agent concept quite extensively. This paper investigate the manner in which agents play a role in these two fields. An agent based app...

Artificial intelligence applications are normally complex, demanding much computing power. This paper presents the multiagent architecture of StormCast, a distributed artificial intelligence application for severe storm forecasting. Important objectives as scaleability, robustness and reusability can be met by partitioning an overall task in subtasks, distribution of the subtasks on different n...