Agents that reduce work and information overload
Communications of the ACM
Analysing expert assistants through interaction diagrams
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Multimedia Tools and Applications
User Interface and Agent Prototyping for Flexible Working
IEEE MultiMedia
COMPSAC '00 24th International Computer Software and Applications Conference
Programming the Mobility Behaviour of Agents by Composing Itineraries
ASIAN '99 Proceedings of the 5th Asian Computing Science Conference on Advances in Computing Science
Agent-Oriented Software Modeling
APSEC '99 Proceedings of the Sixth Asia Pacific Software Engineering Conference
Software Agent Based Approach Towards Tele-Electrocardiography
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Achieving the promise of reuse with agent components
Software engineering for large-scale multi-agent systems
| Hi-index | 4.10 |
Programmers have tools to specify, customize, and control mobile agent behavior. But there are no such tools for nonprogrammers. Developing tools to specify mobile agents and their itineraries is not simple. The fluid state of agent-related standards, emerging proprietary solutions, and workstation technology all influence the design of such tools. This article describes the work being done on the interface to the Agent Inception System. The AIS helps both programmers and nonprogrammers create agent-based applications, including information retrieval, courseware acquisition, videoconference setup, network management, and electronic commerce. The system's interface, the Iconic Modeling Tool (IMT), uses icons to help users visualize and model mobile agents and their itineraries. The IMT is a simple and intuitive tool for modeling mobile agent itineraries. It is designed to be easier for novice users to learn than an agent scripting language or a programming language like Java. As yet there are no formal statistics on user interaction with the IMT, but in preliminary experiments, users were able to create even a complex agent in less than four minutes.