Heterogeneous multi-agent systems are currently used in a wide variety of situations, including search and rescue, military applications, and off-world exploration, however it is difficult to understand the actions of these systems or naturalistically assign these mixed teams to tasks. These agents, which may be human, robot or software, have different capabilities but will need to coordinate effectively with humans in order to operate. The first and largest contributing factor to this challenge is the processing, understanding and representing of elements of the natural world in a manner that can be utilized by artificial agents. A second contributing factor is that current abstractions and robot architectures are ill-suited to address this problem. This dissertation addresses the lack of a high-level abstraction for the naturalistic coordination of teams of heterogeneous robots, humans and other agents through the development of roles.
Roles are a fundamental concept of social science that may provide this necessary abstraction. Roles are not a new concept and have been used in a number of related areas. This work draws from these fields and constructs a coherent and usable model of roles for robotics. This research is focussed on answering the following question: Can the use of social roles enable the naturalistic coordinated operation of robots in a mixed setting? In addition to this primary question, related research includes defining the key concepts important to artificial systems, providing a mapping and implementation from these concepts to a usable robot framework and identifies a set of robot-specific roles used for human-robot interaction. This research will benefit both the artificial intelligence agent and robotics communities. It poses a fundamental contribution to the multi-agent community because it extends and refines the role concept.
The application of roles in a principled and complete implementation is a novel contribution to both software and robotic agents. The creation of an open source operational architecture which supports taskable robots is also a major contribution.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-3263 |
| Date | 01 June 2007 |
| Creators | Long, Matthew T |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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