Wikipedia’s success is often attributed to involving large numbers of contributors who improve the accuracy, completeness and clarity of articles while reducing bias. However, because of the high coordination needed to collaboratively write an article, increasing the number of contributors is costly. We examined how the number of editors in Wikipedia and the coordination methods they use affect article quality. We distinguish between explicit coordination, in which editors plan the article through communication, and implicit coordination, in which a subset of editors set direction by doing the majority of the work. Adding more editors to an article improved article quality only when they used appropriate coordination techniques and was harmful when they did not. Implicit coordination through concentrating the work was more helpful when many editors contributed, but explicit coordination through communication was not. Both types of coordination improved quality more when an article was in a formative stage. These results demonstrate the critical importance of coordination in effectively harnessing the “wisdom of the crowd ” in online production environments.

Proactive information delivery is critical to achieving effective teamwork. However, existing theories do not adequately address proactive information delivery. This paper presents a formal framework for proactive information delivery in agent teamwork. First, the concept of information need is introduced. Second, a new modal operator, InfoNeed is used to represent information needs. The properties of the InfoNeed operator and its relationships to other mental modal operators are examined, four types of information needs are formally identified, and axioms for anticipating the information needs of other agents are proposed and justified. Third, the axiom characterizing chains of helpful behavior in large agent teams is given. Fourth, the semantics for two proactive communicative acts (ProInform and 3PTSubscribe) is given using a reformulation of the Cohen-Levesque semantics for communicative acts in terms of the SharedPlans formalism of Grosz and Kraus. The work in this paper not only provides a better understanding of the underlying assumptions required to justify proactive information delivery behavior, but also provides a coherent basis for the specification and design of agent teams with proactive information delivery capabilities.

In this paper, we describe an interface agent, two different route planning agents and a pilot study which examined whether these agents could support a team planning task. The MokSAF interface agent links an Artificial Intelligence (AI) route-planning agent to a Geographic Information System (GIS). The user specifies a start and an end point and the route-planning agent finds a minimum cost path between the points. The user is allowed to define additional "intangible" constraints (not due to terrain characteristics) corresponding to geographic regions, which can be used to steer the agent's behavior in a desired direction. A second agent (the naive route planning agent, or Naive RPA) has access to the same knowledge of the terrain and cost functions available to the Autonomous RPA, but uses this knowledge to critique paths specified by the user. We hypothesize that as the complexity of intangible aspects of a planning problem increase, the Naive RPA will improve in relative performanc...

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 -- CAST (Collaborative Agents for Simulating Teamwork), was implemented to allow agents in a team to anticipate the information needs of teammates and help them with their information needs proactively, effectively, and timely. 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.

Psychological studies about human teamwork have shown that members of an effective team can often anticipate needs of other teammates and take appropriate actions accordingly. CAST is a teamwork model that enables agents in a team to anticipate information needs of teammates, whether they are software agents or human agents. Based on such needs, agents can choose to assist teammates through proactive communications and information delivery. In this paper, we establish the formal foundation of such proactive behavior using SharedPlan theory. We show that the proactive information delivery behavior of agents can be derived from the assist axiom in SharedPlan theory. This formal foundation of proactive information delivery behavior is critical not only for understanding the underlying assumptions required to justify the behavior but also for studying the impact of an agent's belief about other teammates' observability on the agent's choice for proactive information delivery actions.

The success of Wikipedia has demonstrated the power of peer production in knowledge building. However, unlike many other examples of collective intelligence, tasks in Wikipedia can be deeply interdependent and may incur high coordination costs among editors. Increasing the number of editors increases the resources available to the system, but it also raises the costs of coordination. This suggests that the dependencies of tasks in Wikipedia may determine whether they benefit from increasing the number of editors involved. Specifically, we hypothesize that adding editors may benefit low-coordination tasks but have negative consequences for tasks requiring a high degree of coordination. Furthermore, concentrating the work to reduce coordination dependencies should enable more efficient work by many editors. Analyses of both article ratings and article review comments provide support for both hypotheses. These results suggest ways to better harness the efforts of many editors in social collaborative systems involving high coordination tasks. Author Keywords Wikipedia, wiki, social collaboration, collective

Extreme events such as natural or technological disasters challenge society's capabilities for planning and response. While advanced technologies and modeling techniques continue to expand how society can limit and manage extreme events, flexibility and an ability to improvise remain crucial in responding to them. By analyzing a case from the response to the 2001 World Trade Center attack, this paper develops a set of requirements for computer-based systems intended to support improvisation in response to extreme events. The particular goal of this analysis is to identify methods for providing cognitive-level support for organizations in determining when and how to improvise. Key Words Extreme Events, Improvisation, Emergency Response To appear in Decision Support Systems 323 Martin Luther King, Jr. Blvd, Newark, NJ 07102. Email: mendonca@njit.edu 1

Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human/agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork--- shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.

Sharing common goals and acting cooperatively are critical issues in multi-agent 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 multi-agent 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

The capabilities for agents in a team to anticipate information needs of teammates and proactively offer relevant information are highly desirable. However, such behaviors have not been fully prescribed by existing agent theories. We attempt to establish a theory about proactive information exchanges based on the SharedPlan framework and Cohen and Levesque’s formalization of communicative actions. We first formally specify two types of information needs. A new performative called P roInform is introduced by extending the semantics of Inform to include the speaker’s belief about the information needs of the addressee. For agents in a team containing subteams to achieve proactive information exchanges, we define the semantics of “subscribe” through the third party (e.g., a broker agent). We also show that proactive information exchanges using these communicative actions can be derived as assist behaviors from the theory. The framework not only serves as a formal specification for designing agent architectures, algorithms, and applications that support proactive information exchanges among agents in a team, but also offers opportunities for extending existing agent communication protocols to support proactive teamwork.