A framework for knowledge-based team training

Teamwork is crucial to many disciplines, from activities such as organized sports to economic and military organizations. Team training is difficult and as yet there are few automated tools to assist in the training task. As with the training of individuals, effective training depends upon practice and proper training protocols. In this research, we defined a team training framework for constructing team training systems in domains involving command and control teams. This team training framework provides an underlying model of teamwork and programming interfaces to provide services that ease the construction of team training systems. Also, the framework enables experimentation with training protocols and coaching to be conducted more readily, as team training systems incorporating new protocols or coaching capabilities can be more easily built. For this framework (called CAST-ITT) we developed an underlying intelligent agent architecture known as CAST (Collaborative Agents Simulating Teamwork). CAST provides the underlying model of teamwork and agents to simulate virtual team members. CAST-ITT (Intelligent Team Trainer) uses CAST to also monitor trainees, and support performance assessment and coaching for the purposes of evaluating the performance of a trainee as a member of a team. CAST includes a language for describing teamwork called MALLET (Multi-Agent Logic Language for Encoding Teamwork). MALLET allows us to codify the behaviors of team members (both as virtual agents and as trainees) for use by CAST. In demonstrating CAST-ITT through an implemented team training system called TWP-DDD we have shown that a team training system can be built that uses the framework (CAST-ITT) and has good performance and can be used for achieving real world training objectives

Proactive communication in multi-agent teamwork

Sharing common goals and acting cooperatively are critical issues in multiagent teamwork. Traditionally, agents cooperate with each other by inferring others' actions implicitly or explicitly, based on established norms for behavior or on knowledge about the preferences or interests of others. This kind of cooperation either requires that agents share a large amount of knowledge about the teamwork, which is unrealistic in a distributed team, or requires high-frequency message exchange, which weakens teamwork efficiency, especially for a team that may involve human members. In this research, we designed and developed a new approach called Proactive Communication, which helps to produce realistic behavior and interactions for multiagent teamwork. We emphasize that multi-agent teamwork is governed by the same principles that underlie human cooperation. Psychological studies of human teamwork have shown that members of an effective team often anticipate the needs of other members and choose to assist them proactively. Human team members are also naturally capable of observing the environment and others so they can establish certain parameters for performing actions without communicating with others. Proactive Communication endows agents with observabilities and enables agents use them to track othersâ mental states. Additionally, Proactive Communication uses statistical analysis of the information production and need of team members and uses these data to capture the complex, interdependent decision processes between information needer and provider. Since not all these data are known, we use their expected values with respect to a dynamic estimation of distributions. The approach was evaluated by running several sets of experiments on a Multi- Agent Wumpus World application. The results showed that endowing agents with observability decreased communication load as well as enhanced team performance. The results also showed that with the support of dynamic distributions, estimation, and decision-theoretic modeling, teamwork efficiency were improved

Role-based and agent-oriented teamwork modeling

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