Sheila B. Banks

Using explicit requirements and metrics for interface agent user model correction

The complexity of current computer systems and software vrarrants research into methods to decrease the cognitive load on users. Determining horr to get the right information into the right form vrith the right tool at the right time has bccomc a monumental task one necessitating intelligent intarfacc agents vlith the ability to predict the users’ needs and intent, An accurate user model is considered necessary for effective prediction of user intent. Methods for maintaining nccurato user models is the main thrust of this paper. We describe an approach for dynamically correcting an interface ngent’s user model based on utility theory. We explicitly take into account an agent’s requirements and metrics for mc,asuring the agent’s effectiveness of meeting those requirements, Using these requirements and metrics, me devclop a requirements utility function that determines when a user model should be corrected and how. We present a correction model based on a multi-agent bidding process and the aforementioned metrics and utility function. Finally, me discuss several critical research issues concerning the use of user models that open fertile ground for future research.

GESIA: uncertainty-based reasoning for a generic expert system intelligent user interface

Generic expert systems are reasoning systems that can be used in many application domains, thus requiring domain independence. The user interface for a generic expert system must contain intelligence in order to maintain this domain independence and manage the complex interactions between the user and the expert system. This paper explores the uncertainty-based reasoning contained in an intelligent user interface called GESIA. GESIAs interface architecture and dynamically constructed Bayesian network are examined in detail to show how uncertainty-based reasoning enhances the capabilities of this user interface.

The distributed mission training integrated threat environment system architecture and design

We describe the architecture, design, components, and functionality of the Distributed Mission Training Integrated Threat Environment (DMTITE) software. The DMTITE architecture and design support the development and run-time operation of computer-generated actors (CGAs) in distributed simulations. The architecture and design employ object-oriented techniques, component software, object frameworks, containerization, and rapid prototyping technologies. The DMTITE architecture and design consist of highly modular components where interdependencies are well defined and minimized. DMTITE is an open architecture and open design, and most component and framework code is open source. The DMTITE architecture and design have been implemented (including all system components and frameworks) and currently support a number of types of computer-generated actors. The DMTITE architecture, design, and implementation are capable of supporting multiple reasoning, vehicle dynamics, skill level, and migration requirements for any type of CGA.

Enhancing behavioral fidelity within distributed virtual environments

For a computer generated force (CGF) application to be useful in training environments, it must exhibit complex, realistic behavior within the battlespace. To achieve this level of fidelity, it must operate at multiple skill levels and exhibit competency at assigned missions. CGF applications must also have adaptable decisions mechanisms and behaviors even when operating under uncertainty and the application must learn from past experience. Furthermore, simply correct performance of individual entity behaviors is not sufficient. Issues related to complex inter entity behavioral interactions, such as the need to maintain formation and share information, must also be considered. To achieve these necessary capabilities, an extensible software architecture, an expandable knowledge base, and an adaptable decision making mechanism are required. Our labs have addressed these issues in the context of the Automated Wingman (AW) project. The AW is based on fuzzy logic, the Common Object DataBase (CODE) software architecture, and hierarchical knowledge structure. Decision making is founded on multi layered, fuzzy logic controlled situational analyses combined with adversarial game tree techniques.

Towards an adaptive man-machine interface for virtual environments

We describe an approach to developing an adaptive virtual environment user interface to enable the user to perform a wide variety of tasks while immersed in the virtual environment. Currently, virtual environment operation places an unmanageable cognitive burden upon the user. While some advances in user interface design can alleviate some of this problem, the basic problem of information overload can not be adequately addressed solely through development of a better interface or provision of ad hoc decision support tools. We contend that a comprehensive design approach to the interface can improve user access to the virtual environment display parameters, analysis reports, conferencing and collaboration capabilities, intelligent agents for user assistance, motion and orientation controls, recording devices, and situation awareness aids. Our intelligent interface research effort, called Symbiotic Information Reasoning and Decision Support (SIRDS), addresses the issues related to the design and development of an adaptive, intelligent, learning man machine interface. Construction of the interface requires a mix of traditional human computer interaction, data visualization, and intelligent agents within a software engineering framework. The framework supports the symbiosis of human cognition and computational power that is required to deal with complex virtual environments.

The Solar System Modeler

The authors undertook the Solar System Modeler project to improve comprehension and appreciation of the size, complexity, and splendor of the solar system. To do so, the Solar System Modeler must (1) accurately portray the orbital behavior of satellites, planets, comets, and other celestial bodies, and (2) function in a distributed virtual environment. Additionally, the system needs to: provide a flexible, 3D graphical user interface for immersive operation; assist the user in comprehending the state of the virtual environment; accurately portray the stars and their locations; graphically model all bodies throughout the solar system in 3D and to the same scale; and maintain an interactive frame rate. They describe how they met these requirements.

The Virtual SpacePlane

The paper discusses the Virtual SpacePlane, a virtual prototype of the US Air Forces Manned SpacePlane, which operates in a realistic distributed virtual environment. The Manned SpacePlane (MSP) project is in the initial stages of evaluating the benefit of using military spaceplanes for a variety of low-earth orbit and atmospheric missions. The baseline assumption for the MSP project is that we will need to achieve safe, reliable, affordable, and routine access to space for short notice and worldwide deployment of space assets in the near future.

Computer generated intelligent companions for distributed virtual environments

The employment of computer generated forces (CGFs) within distributed virtual environments (DVEs) dramatically increases the number of entities in a simulated training environment. However, current CGF limitations produce behaviours that can be defeated using methods ineffective against humans. Our research focuses on developing aircraft CGFs. It is necessary to deal with uncertainty, ambiguity, and approximation. The Fuzzy Wingman (FW) relies on fuzzy logic to provide these abilities. In this manner, the FW presents a reasonable approach to effectively populating the simulated training environment with low cost CGFs while maintaining the realism of training with human controlled entities.

An Architecture to Support Large Numbers of Computer-Generated Actors for Distributed Virtual Environments

A variety of challenges exist in the design of systems that can be used to host a wide variety of computer-generated actors (CGAs) that possess believable behaviors. The challenges arise in the areas of system architecture and design, knowledge-base design, decision-making mechanisms, and the distributed virtual environment (DVE) network interface. These challenges are especially significant if the DVE is to be used for training, because accurate training is essential to the ready application of training experience to real-world situations. The project described in this paper was undertaken to improve the quality of threat CGAs in DVEs utilized for aircrew training. In this paper, we describe the system and the reasons for its genesis. We present the system requirements, system architecture, component-wise decomposition of the system design, and structure of the major components of the decision mechanism. We conclude with a summary of our results to date and recommendations for further research.

Advancing botnet modeling techniques for military and security simulations

Simulation environments serve many purposes, but they are only as good as their content. One of the most challenging and pressing areas that call for improved content is the simulation of bot armies (botnets) and their effects upon networks and computer systems. Botnets are a new type of malware, a type that is more powerful and potentially dangerous than any other type of malware. A botnets power derives from several capabilities including the following: 1) the botnets capability to be controlled and directed throughout all phases of its activity, 2) a command and control structure that grows increasingly sophisticated, and 3) the ability of a bots software to be updated at any time by the owner of the bot (a person commonly called a bot master or bot herder.) Not only is a bot army powerful and agile in its technical capabilities, a bot army can be extremely large, can be comprised of tens of thousands, if not millions, of compromised computers or it can be as small as a few thousand targeted systems. In all botnets, their members can surreptitiously communicate with each other and their command and control centers. In sum, these capabilities allow a bot army to execute attacks that are technically sophisticated, difficult to trace, tactically agile, massive, and coordinated. To improve our understanding of their operation and potential, we believe that it is necessary to develop computer security simulations that accurately portray bot army activities, with the goal of including bot army simulations within military simulation environments. In this paper, we investigate issues that arise when simulating bot armies and propose a combination of the biologically inspired MSEIR infection spread model coupled with the jump-diffusion infection spread model to portray botnet propagation.