Knowledge Base Support for Design and Synthesis of Multi-Agent Systems

Abstract

AgentTool is an AFiT-produced, AFOSR-sponsored multi-agent system (MAS) development tool intended for production of MASs that meet military requirements. This research focuses on enabling MAS design and synthesis tools like agentTool to store, retrieve, and filter persistent, reusable, and reliable agent domain knowledge. This "enabling" is vital if such tools are expected to produce consistent, maintainable, and verifiable agent applications on short timetables. Enabling requires: 1) modeling the agent knowledge domain, 2) designing and employing a persistent knowledge base, and 3) bridging that domain model to the knowledge base with an extensible domain interchange grammar. The achieved interchange grammar, called Multi-Agent Markup Language (MAML), is presented and shown to be capable of representing MAS design knowledge in a concise and easily parsed form that is readily stored and retrieved in the knowledge base. The selected knowledge base, called the Agent Random-Access Meta- Structure (ARAMS), is shown to support MAML and operate in a distributed environment that permits sharing of agent development knowledge between various tools and tool instances. Tests of MAML and ARAMS with agentTool are summarized, and related future work suggested.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2000
Accession Number
ADA380744

Entities

People

  • Marc J. Raphael

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cognitive Science
  • Computer Languages
  • Computer Program Documentation
  • Computer Program Reliability
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Electronic Commerce
  • Information Systems
  • Multiagent Systems
  • Ontologies
  • Reasoning
  • Software Agents
  • Software Development
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Linguistics
  • Software Engineering.