Shuang Sun

Agents with shared mental models for enhancing team decision makings

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.

Merging workflows: a new perspective on connecting business processes

This paper describes the concept of workflow merge and methods for merging business processes. We grouped merges in four categories according to the type of merge: sequential, parallel, conditional, and iterative, and describe the corresponding algorithms for performing these operations. We give results that allow us to determine whether a merge operation is sound. It is shown that to avoid invalid merges, one should choose merge points between which a sub-workflow, called a merge region, is well structured. These findings can provide useful guidance for future workflow merge research. We also raise issues of more complex merge problems, such as merge conflicts, semantic ambiguities and workflow splits.

Extending the recognition-primed decision model to support human-agent collaboration

There has been much research investigating team cognition, naturalistic decision making, and collaborative technology as it relates to real world, complex domains of practice. However, there has been limited work in incorporating naturalistic decision making models for supporting distributed team decision making. The aim of this research is to support human decision making teams using cognitive agents empowered by a collaborative Recognition-Primed Decision model. In this paper, we first describe an RPD-enabled agent architecture (R-CAST), in which we have implemented an internal mechanism of decision-making adaptation based on collaborative expectancy monitoring, and an information exchange mechanism driven by relevant cue analysis. We have evaluated R-CAST agents in a real-time simulation environment, feeding teams with frequent decision-making tasks under different tempo situations. While the result conforms to psychological findings that human team members are extremely sensitive to their workload in high-tempo situations, it clearly indicates that human teams, when supported by R-CAST agents, can perform better in the sense that they can maintain team performance at acceptable levels in high time pressure situations.

RPD-enabled agents teaming with humans for multi-context decision making

Team decision making under stress involving multiple contexts is an extremely challenging issue faced by various real world application domains. This research is targeted at coupling cognitive agent technology and human-centered teamwork to address the informational challenges associated with Command and Control (C2) teams in contemporary military operations. Two sets of experiments, each with various settings of context switching frequencies and tasking complexities, were conducted. To ensure that the human subjects were familiar with the C2 context, they were selected from US Army ROTC (Reserve Officer Training Corps) students. Experiments on C2 teams that involve human subjects only were conducted first. We observed the decision making behavior of human subjects and incorporated expert behaviors into R-CAST---an agent architecture built upon a naturalistic decision making model that captures how domain experts make decisions based on experiences and situational similarity recognition. We then conducted another set of experiments with R-CAST agents as teammates and decision aids for human subjects. The results show that RPD-enabled agents can significantly improve the tasking capacity of C2 teams in multi-context decision making under stress. It also suggests that higher demand situations require more competent teammates.

Information supply chain: a unified framework for information-sharing

To balance demand and supply of information, we propose a framework called “information supply chain” (ISC). This framework is based on supply chain management (SCM), which has been used in business management science. Both ISC and SCM aim to satisfy demand with high responsiveness and efficiency. ISC uses an information requirement planning (IRP) algorithm to reason, plan, and satisfy needers with useful information. We believe that ISC can not only unify existing information-sharing methods, but also produce new solutions that enable the right information to be delivered to the right recipients in the right way and at the right time.

Proactive information gathering for homeland security teams

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

Supporting anti-terrorist analyst teams using agents with shared RPD process

Antiterrorist analysts often need to work in teams with the requirement to analyze voluminous amounts of dynamic information in order to assess potential terrorist threats. Analysts have a high cognitive demand complicated by factors that typically the information has restrict access and requires special expertise for interpretation. The goal of this research is to enhance team performance by modeling and implementing a cognitive agent architecture capable of proactively seeking, linking and sharing information using knowledge and experience distributed among team members. The agent architecture is empowered by a collaborative RPD model - a novel team-based naturalistic decision making process derived from Kleins recognition-primed decision framework.

Multi-agent information dependence

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.

Implications of agent-based supply chain games

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.

ON MODELING AND SIMULATING AGENT TEAMWORK IN CAST

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.