Xiaocong Fan

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.

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.

A theoretical framework on proactive information exchange in agent teamwork

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.

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.

Human–Agent Collaboration for Time-Stressed Multicontext Decision Making

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.

MALLET - a multi-agent logic language for encoding teamwork

MALLET, a multi-agent logic language for encoding teamwork, is intended to enable expression of teamwork emulating human teamwork, allowing experimentation with different levels and forms of inferred team intelligence. A consequence of this goal is that the actual teamwork behavior is determined by the level of intelligence built into the underlying system as well as the semantics of the language. In this paper, we give the design objectives, the syntax, and an operational semantics for MALLET in terms of a transition system. We show how the semantics can be used to reason about the behaviors of team-based agents. The semantics can also be used to guide the implementation of various MALLET interpreters emulating different forms of team intelligence, as well as formally study the properties of team-based agents specified in MALLET. We have explored various forms of proactive information exchange behavior embodied in human teamwork using the CAST system, which implements a built-in MALLET interpreter.

RPD-based Hypothesis Reasoning for Cyber Situation Awareness

Intelligence workers such as analysts, commanders, and soldiers often need a hypothesis reasoning framework to gain improved situation awareness of the highly dynamic cyber space. The development of such a framework requires the integration of interdisciplinary techniques, including supports for distributed cognition (human-in-the-loop hypothesis generation), supports for team collaboration (identification of information for hypothesis evaluation), and supports for resource-constrained information collection (hypotheses competing for information collection resources). We here describe a cognitively-inspired framework that is built upon Klein’s recognition-primed decision model and integrates the three components of Endsley’s situation awareness model. The framework naturally connects the logic world of tools for cyber situation awareness with the mental world of human analysts, enabling the perception, comprehension, and prediction of cyber situations for better prevention, survival, and response to cyber attacks by adapting missions at the operational, tactical, and strategic levels.

The semantics of MALLET–An agent teamwork encoding language

MALLET is a team-oriented agent specification and programming language. In this paper, we define an operational semantics for MALLET in terms of a transition system. The semantics can be used to guide the implementation of MALLET interpreters, and to formally study the properties of team-based agents specified in MALLET.

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.

Proactive Communications in Agent Teamwork

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. To establish a theory about proactive information exchanges, we first introduces the concept of ”information-needs”, then identify and formally define the intentional semantics of two proactive communicative acts, which highly depend on the speaker’s awareness of others’ information-needs. It is shown that communications using these proactive performatives can be derived as helping behaviors. Conversation policies involving these proactive performatives are also discussed. The work in this paper may serve as a guide for the specification and design of agent architectures, algorithms, and applications that support proactive communications in agent teamwork.