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

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.

/pdf/mallet-a-multi-agent-logic-language-for-encoding-teamwork-3x2uol1768.pdf

MALLET - a multi-agent logic language for encoding teamwork

: The objective for the UBOT project was to reduce the barriers for adoption of Semantic Web technology. The target adopters were DoD and Intelligence community software practitioners. The UBOT project focused on three major barriers: Ontology engineering is hard, Semantic markup creation is time consuming and expensive, and Semantic Web application engineering trade-offs are not well understood. The approach to reducing these barriers was to apply software engineering principles: automated tools, formal methods and software architecture. There were four research focus areas in this project: Ontology engineering; Automated markup generation; Reasoning architectures and benchmarks; and Semantic Web architectures and design patterns.

DARPA Agent Markup Language (DAML) Unified Modeling Language (UML)-Based Ontology Toolset (UBOT)

User-generated content can help the growth of linked data. However, we lack interfaces enabling ordinary people to author linked data. Secondly, people have multiple perspectives on the same concept and different contexts. Thirdly, not enough ontologies exist to model various data. Therefore, we propose an approach to enable people to share various data through an easy-to-use social platform. Users define their own concepts and multiple conceptualizations are allowed. These are consolidated using semi-automatic schema alignment techniques supported by the community. Further, concepts are grouped semi-automatically by similarity. As a result of consolidation and grouping, informal lightweight ontologies emerge gradually. We have implemented social software, called StYLiD, to realize our approach. It can serve as a platform motivating people to bookmark and share different things. It may also drive vertical portals for specific communities with integrated data from multiple sources. Experimental observations support the validity of our approach.

Community-Driven Linked Data Authoring and Production of Consolidated Linked Data

Capturing human resources, their capabilities and dynamics can greatly contribute to an efficient and effective organization mission goals accomplishment. In practice however, this is often done in an ad-hoc manner leading to a big gap between the organizational specification model (at the design time) and its instance model (during the life cycle). In this paper we present a generic organizational model that captures the human resources, their operational and strategic capabilities, and adaptive coalition formation. The key elements of the model are capabilities, roles, agents and coalitions. We show how the resulting model can be used for role functionalities and performance control, as well as for capturing the knowledge in context-aware applications which is often present in an implicit manner.

An Aggregation of Agents, Roles and Coalition Formation to Support Collaborative and Dynamic Organizations

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

Proactive information gathering for homeland security teams

A synthetic literature review on analytics to support curriculum improvement in higher education

I magine a software assistant agent (AA) that proactively provides relevant concise personalized information to an analyst or first responder and collaborates with other AAs to share knowledge and arrange person-to-person contact. The following descriptive scenario provides an example application of this concept. A report of an explosion at a chemical plant is received by a homeland security (HLS) analyst, local firefighters, and state police. The AA for the analyst “reads” the report and immediately retrieves information about what chemicals exist in the plant and a map of nearby dangerous and vulnerable facilities. The firefighters arrive on the scene and identify the sector from which flames and smoke are emanating. An AA provides the firefighters with information on what chemicals are likely to be burning, how to extinguish the fire, and the potential health hazards of the smoke. The AA notifies the HLS analyst that it is a harmless gas. Plant personnel tell state police they saw a suspicious individual in a car in the parking lot. The police enter the license plate number of the suspicious car and the analyst AA immediately searches for aliases of the owner and links to terrorist organizations. A link is found and the HLS analyst’s AA searches for an AA of an expert on that terrorist group. The terrorist expert AA notifies the HLS AA that an associate of the suspicious person is a chemical engineer that works in a nearby plant where another explosion has just been reported. The HLS AA discovers that if the two smoke plumes intersect they will create a deadly acid mist. The AA plots the smoke plumes on a map and notifies the HLS analyst that the combined plume will reach a crowded sports stadium in approximately 15 minutes. The AA immediately initiates a phone call between the HLS analyst and stadium security. This scenario illustrates how a team of software agents could support a team of people to accomplish a time-critical task related to U.S. homeland security activities. One of the major technical issues in designing software agents with these capabilities is they need to be By Paul Kogut, John Yen, Yui Leung, Shuang Sun, Rui Wang, Ted Mielczarek, and Ben Hellar

/pdf/proactive-information-ga-t-homeland-security-teams-3y5gystujx.pdf

Proactive information ga t homeland security teams

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Proactive information gathering for homeland security teams

A synthetic literature review on analytics to support curriculum improvement in higher education

Proactive information ga t homeland security teams