Teamwork is the joint work of individuals who act together productively. Recent technological advancement, global competition and world events made teamwork vitally important to the success of many organizations in both military and civilian sectors. Team training is arguably one of the most intensively studied topics for cognitive science researchers in the past decades, yet there are few effective software tools that automate team performance assessment and coaching. 
The goal of this research is to develop an intelligent training framework where software agents are used to automate team performance assessment and coaching, with a focus on helping behavior. One of the design challenges in coaching for team training is that the performance of a team is affected by multiple factors, which include the quality of the team's plan and each individual's execution of the plan. To address this difficulty, our framework uses a two-phase training protocol that provides coaching for two phases: a mission planning phase and a mission execution phase. We adopt two user modeling approaches (overlay and error taxonomy) in Intelligent Tutoring System (ITS) for the two phases, according to different complexity involved in modeling expert behavior. In the planning phase, intelligent coaching feedback is generated based on an expert model for resource allocation. In the execution phase, coaching feedback is generated based on error taxonomy to assess team's execution performance of their planned activities. Due to the broad scope of team training, this research stresses one important dimension of teamwork---the helping behavior among team members, in case of an unbalanced workload and resource distribution. Coaching feedback is provided in a debriefing session at the end of each mission execution with a goal of improving trainee performance during the next mission. 
We have implemented the framework within a team-based agent architecture and applied it to train helping behaviors for a simulated command and control (C2) task. To evaluate the effectiveness of the agent-based team training approach, we designed and conducted a human subject experiment that applied the agent-based two-phase training protocol and provided teams in the experiment group with feedback generated by the coaching agents. Results have suggested that the coaching agents have a positive impact on trainees' learning of how to effectively helping each other to achieve mission success in time-critical and complex task domains.

/pdf/intelligent-coaching-agents-for-enhancing-helping-behavior-3mn78olpfq.pdf

Intelligent coaching agents for enhancing helping behavior in human teamwork

The increased prevalence of network-enabled supply chains and out-sourcing of business processes suggests a stronger role for simulation tools, such as multi-agent systems, in supply chain management. We report on a new supply chain management game in the 2003 Trading Agent Competition and the design and experiences of our agent, PSUTAC. We discuss how using a shared mental model approach can help SCM designers address the role of information flow in an uncertain market environment. We conclude with a discussion about future implications to SCM of such trading agent simulations.

/pdf/implications-of-agent-based-supply-chain-games-4pnmo7y3ey.pdf

Implications of agent-based supply chain games

The objective of this research is to develop an agent-based model of transactive memory systems 
(TMS - meta-knowledge of expertise and knowledge in a team) simulating software development 
teams using two different software development methodologies. Waterfall (a structured methodology with a series of large discrete phases) and the Agile. eXtreme programming (XP - more recent. 
dynamic, and tuned to change and flexibility). There does exist research relating to TMS; 
comparisons of software development methodologies; cognitive processes of software development 
teams; and also agent-based modelling of social and cognitive systems. This is interdisciplinary 
research spanning psychology and computer science aiming to consolidate these discrete streams of 
research. 

The model evaluated the parameters of small/large tasks. and working solo/in pairs to investigate the 
effect on TMS, knowledge and team output. Over three sirnulaiions. increasing in cognitive realism. 
the model introduced greater complexity and novelty to the agents work. and various initial 
conditions of team knowledge and team member familiarity. 

The results illustrated a number of differences in TMS, knowledge processes and output between XP 
and Waterfall teams. The main findings indicate that as the novelty and complexity of the task 
increases the use of some XP techniques can lower the reduction in output. Also the dependence on 
TMS accuracy for teams using some XP techniques in complex novel environments is high while the 
team knowledge distribution becomes much more homogenous. This contradicts the literature that 
asserts a positive relationship between TMS accuracy and knowledge heterogeneity. Results also 
suggest that XP techniques can compensate for the advantages relating to team members' prior 
knowledge of each other allowing newly formed X P teams to perform better. 

The results contribute to understanding how knowledge and memory processes in software 
development teams affect team output, and how the adoption of XP practices can produce results that, challenge the established TMS literature.

Agent-based modelling of transactive memory systems and knowledge processes in agile versus traditional software development teams

Effective human teams use overlapping shared mental models for anticipating information needs of teammates and for offering relevant information proactively. The long-term goal of our research is to empower agents with such “shared mental models” so that they can be used to better simulate, train, or support human teams for their information fusion, interpretation, and decisions. Toward this goal, we have developed a team agent architecture called CAST that enables agents to infer information needs of teammates, which further enables agents to assist teammates by proactively delivering needed information to them. In this paper, we focus on two key issues related to proactive information delivery behavior. First, we model the semantics of proactive information delivery as an attempt (called ProAssert), which extends the performative Assert in Joint Intention Theory. Second, we introduce a decision-theoretic approach for reasoning about whether to act on a potential proactive assert. Experimental results suggested that the decision-theoretic communication strategy enhances the team performance. The formal semantics and the decision-theoretic communication strategies together provide a sound and practical framework that enables further studies regarding proactive information delivery for supporting the decision making of a team involving human and agents.

/pdf/on-modeling-and-simulating-agent-teamwork-in-cast-528ev319om.pdf

On modeling and simulating agent teamwork in cast

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On modeling and simulating agent teamwork in cast