(1997). Team Effectiveness and Decision Making in Organizations. Quality Management Journal: Vol. 4, No. 2, pp. 16-17.

Team Effectiveness and Decision Making in Organizations

Proactive information sharing is a challenging issue faced by intelligence agencies in effectively making critical decisions under time pressure in areas related to homeland security. Motivated by psychological studies on human teams, a team-oriented agent architecture, Collaborative Agents for Simulating Teamwork (CAST), was implemented to allow agents in a team to anticipate the information needs of teammates and help them with their information needs proactively and effectively. In this paper, we extend CAST with a decision-making module. Through two sets of experiments in a simulated battlefield, we evaluate the effectiveness of the decision-theoretic proactive communication strategy in improving team performance, and the effectiveness of information fusion as an approach to alleviating the information overload problem faced by distributed decision makers.

Agents with shared mental models for enhancing team decision makings

Multicontext team decision making under time stress is an extremely challenging issue faced by various real-world application domains. In this paper, we employ an experience-based cognitive agent architecture (called R-CAST) to address the informational challenges associated with military command and control (C2) decision-making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C2 decision-making teams. Members from a U.S. Army Reserve Officer Training Corps organization were randomly recruited as human participants. They were grouped into ten human-human teams, each composed of two participants, and ten human-agent teams, each composed of one participant and two R-CAST agents, as teammates and decision aids. The statistical inference of experimental results indicates that R-CAST agents can significantly improve the performance of C2 teams in multicontext decision making under varying time-stressed situations.

Human–Agent Collaboration for Time-Stressed Multicontext Decision Making

Urban traffic incident management in a digital society. An actor-network approach in information technology use in urban Europe

Heterogeneous sensor networks are increasingly deployed to support users in the field requiring many different kinds of sensing tasks. There may be multiple alternative kinds of sensors suitable for a given task. Sensing tasks might compete for the exclusive usage of available sensors. Such an environment is highly dynamic with users moving and generating tasks at different rates. Users typically lack the time and expertise to manually decide which are the best sensors for their tasks. We need therefore to design a distributed system to automatically allocate sensors to tasks. We formalize this problem as Multi-Sensor Task Allocation (MSTA) and show that the heterogeneity of sensors and tasks requires knowledge-based sensor-task matching. We extend a pre-existent well known coalition formation protocol to propose a novel layered distributed system which by using qualitative and quantitative measures provides allocation flexibility. We demonstrate that it is feasible to perform the knowledge-based sensor-task matching on a user's device by presenting a proof-of-concept mobile app which allows a user in the field to interact with the system. We run simulations to demonstrate that our architecture is scalable and that the allocation quality improves by allowing preemption of sensing resources from ongoing tasks and a reallocation mechanism.

Demo: A distributed architecture for heterogeneous multi sensor-task allocation

Supporting counterterrorism analysts with software agents that dynamically anticipate their information requirements.

Proactive information gathering for homeland security teams

Effective agent teamwork requires information exchange to be conducted in a proactive, selective, and intelligent way. In the field of distributed artificial intelligence, there has been increasing number of research focusing on need-driven proactive communication, both theoretically and practically. Among these works, CAST has realized a team-oriented agent architecture where agents, based on a computational shared mental model, are able to anticipate teammates' information needs and proactively deliver relevant information to the needers in a timely manner. However, the first implementation of CAST takes little consideration of the dynamics of the anticipated information needs, which can change in various ways as the context develops. In this paper we describe a novel mechanism for organizing and managing the "context" of information needs. This allows agents to dynamically activate and deactivate information needs progressively. It has been shown that the two-level context-centric approach can enhance team performance considerably.

/pdf/context-centric-needs-anticipation-using-information-needs-1u8t5isb5p.pdf

Context-Centric Needs Anticipation Using Information Needs Graphs

There has been an increasing interest in reasoning about multi-agent information dependence, which is very important for supporting social reasoning in multi-agent cooperations. In this paper we seek to characterize the nature of multi-agent information dependence in general, and investigate ways of using information dependence knowledge in agent teamwork settings. We also describe a tool that can facilitate humans to dynamically manipulate information dependence.

/pdf/multi-agent-information-dependence-5d066q5ztm.pdf

Multi-agent information dependence

Delivering only those information relevant to the needs of teammates is critical for avoiding information overload. Need-driven proactive information delivery has thus been widely investigated in diverse areas spanning from psychology, AI, to Web-based services. CAST, a team-oriented agent architecture, realized proactive information delivery as a built-in agent behavior, which is derived by the capability of anticipating others' information-needs based on a computational shared mental model. However, the first implementation of CAST is limited in managing the dynamic activation and deactivation of teammates' information-needs. This work addresses this issue by proposing a mechanism for organizing and managing the "context" of information-needs, which facilitates information-needs to be activated and deactivated dynamically. We evaluate the proposed approach by comparing its performance with agent teams using only reactive communications (e.g., ask/reply) and with the original CAST agent teams.

/pdf/context-centric-proactive-information-delivery-542eiokjuh.pdf

Context-centric proactive information delivery

Effective human teams often benefit from proactivity through members' capability of anticipating different needs of teammates. In this paper, we focus on three issues related to the behavior of proactive information delivery. Two types of information needs are identified, the intentional semantics of ProInform is given, and priliminary experiments are carried out to study how different strategies for choosing ProInform impact team performance. The work presented in this paper provide a sound and practical framework that enables further studies regarding proactive information delivery.

/pdf/formal-semantics-and-communication-strategies-for-proactive-2gu03xc7k6.pdf

Formal semantics and communication strategies for proactive information delivery among team-based agents

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