C4.5: programs for machine learning
C4.5: programs for machine learning
Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
An adaptive interactive agent for route advice
Proceedings of the third annual conference on Autonomous Agents
All Agents Are Not Created Equal
IEEE Internet Computing
Distributed Intelligent Agents
IEEE Expert: Intelligent Systems and Their Applications
Journal of Artificial Intelligence Research
Agent-based computing: promise and perils
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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With the proliferation of software agents and smart hardware devices there is a growing realization that large-scale problems can be addressed by integration of such stand-alone systems. This has led to an increasing interest in integration infrastructures that enable a heterogeneous variety of agents and humans to work together. In our work, this infrastructure has taken the form of an integration architecture called Teamcore. We have deployed Teamcore to facilitate/ enable collaboration between different agents and humans that differ in their capabilities, preferences, the level of autonomy they are willing to grant the integration architecture, their information requirements and performance. This paper first provides a brief overview of the Teamcore architecture and its current applications. The paper then discusses some of the research challenges we have focused on. In particular, the Teamcore architecture is based on general purpose teamwork coordination capabilities. However, it is important for this architecture to adapt to meet the needs and requirements of specific individuals. We describe the different techniques of architectural adaptation, and present initial experimental results.