Teamwork has become increasingly important in many disciplines. To support
teamwork in dynamic and complex domains, a teamwork programming language and a
teamwork architecture are important for specifying the knowledge of teamwork and for
interpreting the knowledge of teamwork and then driving agents to interact with the
domains. Psychological studies on teamwork have also shown that team members in an
effective team often maintain shared mental models so that they can have mutual
expectation on each other. However, existing agent/teamwork programming languages
cannot explicitly express the mental states underlying teamwork, and existing
representation of the shared mental models are inefficient and further become an
obstacle to support effective teamwork. To address these issues, we have developed a
teamwork programming language called Role-Based MALLET (RoB-MALLET) which
has rich expressivity to explicitly specify the mental states underlying teamwork. By
using roles and role variables, the knowledge of team processes is specified in terms of
conceptual notions, instead of specific agents and agent variables, allowing joint
intentions to be formed and this knowledge to be reused by different teams of agents.
Further, based on roles and role variables, we have developed mechanisms of task
decomposition and task delegation, by which the knowledge of a team process is
decomposed into the knowledge of a team process for individuals and then delegate it to
agents. We have also developed an efficient representation of shared mental models
called Role-Based Shared Mental Model (RoB-SMM) by which agents only maintain
individual processes complementary with others?? individual process and a low level of
overlapping called team organizations. Based on RoB-SMMs, we have developed tworeasoning mechanisms to improve team performance, including Role-Based Proactive
Information Exchange (RoB-PIE) and Role-Based Proactive Helping Behaivors (RoBPHB).
Through RoB-PIE, agents can anticipate other agents?? information needs and
proactively exchange information with them. Through RoB-PHB, agents can identify
other agents?? help needs and proactively initialize actions to help them. Our experiments
have shown that RoB-MALLET is flexible in specifying reusable plans, RoB-SMMs is
efficient in supporting effective teamwork, and RoB-PHB improves team performance.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2540 |
| Date | 01 November 2005 |
| Creators | Cao, Sen |
| Contributors | Volz, Richard A. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1132045 bytes, electronic, application/pdf, born digital |
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