Agent Systems Reference Model
The Agent systems reference model (ASRM) is a layered, abstract description for multiagent systems. As such, the reference model
- provides a taxonomy of terms, concepts and definitions to compare agent systems;
- identifies functional elements that are commin in agent systems;
- captures data flow and dependencies among the functional elements in agent systems; and
- specifies assumptions and requirements regarding the dependencies among these elements.
The ASRM differentiates itself from technical standards, such as Knowledge Interchange Format, Knowledge Query and Manipulation Language, and those of the Foundation for Intelligent Physical Agents in that it defines the required existence of components of a multiagent system; standards prescribe how they are designed.
Technical approach
The ASRM was technically constructed through forensic software analysis of existing agent-based systems. Such fielded systems include JaDE, Cougaar, EMAA, NOMADS, Retsina, A-Globe, among others. In so doing, through empirical evidence, the ASRM motivates its functional breakdown of agent-based systems.
Description of the ASRM layers
ASRM: the Layered Model |
|---|
Agent-based |
History
The ASRM was started in July 2005, with the first draft having been completed in November 2006. Contributors to the document have included Drexel University, Cougaar Software, Global InfoTek (see also: CoABS), Soar Technology (see also: Soar), Penn State University, University of Southern California, University of South Carolina, the Institute for Human Machine Cognition, University of West Florida, BBN Technologies, Telcordia, Lockheed Martin, General Dynamics and others.
See also
- Reference model
- Agent based model
- Multiagent system
- Artificial Intelligence
- Distributed Artificial Intelligence
- Software agent
- Intelligent agent
- Complex systems
Further reading
- Version 1.0a of the ASRM
- The Case for a Reference Model for Agent-Based Systems. Pragnesh Jay Modi, William C. Regli and Israel Mayk. In Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications. June, 2006. Pages 321–325.