Role-based and agent-oriented teamwork modeling.

다국어 초록 (Multilingual Abstract)

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 two reasoning mechanisms to improve team performance, including Role-Based Proactive Information Exchange (RoB-PIE) and Role-Based Proactive Helping Behaviors (RoB-PHB). 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.