Christopher Zhong

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This paper presents a reusable framework for developing adaptive multi-robotic systems for heterogeneous robot teams using an organization-based approach. The framework is based on the Organizational Model for Adaptive Computational Systems (OMACS) and the Goal Model for Dynamic Systems (GMoDS). GMoDS is used to capture system-level goals that drive the(More)
Easy missions approaches to machine learning seek to synthesize solutions for complex tasks from those for simpler ones. In genetic programming, this has been achieved by identifying goals and fitness functions for subproblems of the overall problem. Solutions evolved for these subproblems are then reused to speed up learning, either as automatically(More)
We consider the problem of incremental transfer of behaviors in a multi-agent learning test bed (keep-away soccer) consisting of homogeneous agents (keepers). One method for this incremental transfer is called the easy missions approach, and seeks to synthesize solutions for complex tasks from those for simpler ones. In genetic programming (GP), this has(More)
Easy missions is an approach to machine learning that seeks to synthesize solutions for complex tasks from those for simpler ones. ISLES (Incrementally Staged Learning from Easier Subtasks) [1] is a genetic programming (GP) technique that achieves this by using identified goals and fitness functions for subproblems of the overall problem. Solutions evolved(More)
There is a growing demand for more effective integration of humans and computing systems, specifically in multiagent and multirobot systems. There are two aspects to consider in human integration: (1) the ability to control an arbitrary number of robots (particularly heterogeneous robots) and (2) integrating humans as peers in computing systems instead of(More)
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