Sean Guarino

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

In recent years, much attention has been focused on emerging concepts of decentralized control for the National Airspace System. The introduction of a distributed decision-making environment in a future air traffic management system gives airspace users (pilots and airline operations centers) freedom to change and optimize flight plans in real time, in contrast to the current operational paradigm characterized by centralized decision-making by the air traffic service provider. Such an approach to air traffic operations will require individual decision-makers within the airspace to identify and solve, in real time, routing problems to avoid traffic conflicts, weather cells and Special Use Airspace. In this paper, we apply a modeling and simulation based approach to investigate the potential for a Principled Negotiation-based approach to the distributed air traffic management problem. Another objective is to identify the frequency with which truly collaborative (i.e., negotiation) behavior may be required to solve air traffic conflicts while avoiding regions of airspace. A free flight simulation in Los Angeles Center airspace, based on real traffic and Special Use Airspace data indicates that over 8% of aircraft-to-aircraft conflicts require negotiation. Principal Scientist; kharper@cra.com. Member, AIAA. Author to whom all correspondence should be addressed. ✝ Software Engineer. Member, AIAA. ‡ Software Engineer. § Senior Scientist. Member, AIAA Research Scientist, Automation Concepts Research Branch; Mail Stop 210-10; E-mail: kbilimoria@mail.arc.nasa.gov. Associate Fellow, AIAA. ✝✝ Software Engineer, Raytheon ITSS. Introduction Emerging concepts for future air traffic management (ATM) systems and procedures will dramatically change human roles and tasks in the National Airspace System (NAS). There are several technical initiatives in the ATM community for the development of new concepts of operations to meet the projected airspace demands of the future, while maintaining the overall safety of the NAS. The Free Flight paradigm (RTCA, 1995) gives aircraft operators the freedom to optimize their trajectories in real time, while requiring them to assume responsibility for maintaining safe separation from other aircraft and conforming to any ATM restrictions imposed by the air traffic service provider. One possible approach to implement Free Flight is the Distributed Air/Ground Traffic Management (DAG-TM) concept of operations (NASA, 1999). DAG-TM is characterized by distributed decision-making among pilots, air traffic service providers and airline operational control (AOC) personnel, who work cooperatively to optimize both individual and global operations, while maintaining safety as the highest priority. The introduction of this envisioned distributed decision-making (DDM) environment, however, will have profound implications on pilot/controller/AOC information requirements, roles and responsibilities, allocation of workload, communication, and decisionmaking throughout the ATM system. In order to evaluate the feasibility of such ATM concepts, there is a need to model the new rules and protocols governing individual behavior of the key decision-makers in the air transportation system, including pilots, air traffic controllers and airline dispatchers. Because these decision-makers will ultimately drive overall ATM system performance and safety, any modeling and simulation approach to system analysis must include realistic human behavior representations (HBRs) of the key decision-makers. Furthermore, if we are to capture AIAA Guidance, Navigation, and Control Conference and Exhibit 5-8 August 2002, Monterey, California AIAA 2002-4552 Copyright

Air traffic controller agent model for free flight

The objective of this research was to design and demonstrate an agent-based modeling and analysis tool for evaluating air traffic controller (ATC) performance under free flight air traffic management (ATM), and to develop a distributed decision-making model to investigate the potential for collaborative problemsolving under free flight. We developed a set of intelligent agent models representing ATC and pilot behavior in free flight. These models contain simplified representations of information processing and situation assessment applied to air traffic conflict detection. They also contain detailed air traffic conflict resolution models applying collaborative decisionmaking via inter-agent negotiation. We demonstrated the operation of a limited-scope prototype in a set of simulation trials designed to exercise the multi-agent decision-making model under a spectrum of free flight air traffic configurations. The results showed that our agent models are capable of performing on-line learning to support global situation awareness (SA) in the air traffic environment. The nature of conflict resolution maneuvers negotiated by the agents varied with conflict geometry in a manner consistent with intuitive expectation.

Modeling human reasoning about meta-information

Information, as well as its qualifiers, or meta-information, forms the basis of human decision-making. Human behavior models (HBMs) therefore require the development of representations of both information and meta-information. However, while existing models and modeling approaches may include computational technologies that support meta-information analysis, they generally neglect its role in human reasoning. Herein, we describe the application of Bayesian belief networks to model how humans calculate, aggregate, and reason about meta-information when making decisions.

Cognitive Complexities Impacting Army Intelligence Analysis

The primary goal of this effort was to understand the problems faced by military intelligence analysis personnel as well as how, and to what degree, the identification of these problems could guide the development of computational support systems. To develop this understanding, we performed a literature review, knowledge elicitation interviews and a cognitive task analysis (CTA) in the domain of Army Intelligence Analysis at the Brigade Combat Team. This effort consisted of identifying: (1) the major functions or cognitive tasks entailed in Army Intelligence Analysis; and (2) the complexities in the domain that pose challenges to performance of these cognitive tasks. Identifying the cognitive tasks and the challenges faced in performing those tasks provided a basis for determining opportunities for more effective support of human information processing and decision-making. In this paper, we document selected results of this analysis effort.

Effect of Novel Adaptive Displays on Pilot Performance and Workload

The effect of adaptive display on pilot performance was assessed using objective and subjective measures from a sequence of 2 experiments. Combat aircraft advances have led to dramatic increases in the operational tempo facing the pilot, increasing the likelihood for situation awareness (SA) failures, biases, and information overload. Prior research suggests that adaptive interface might help to address this issue. We designed and evaluated 2 interfaces targeted at problem areas for pilots: a weapons employment zone (WEZ) display designed to support awareness of combat geometry, and an adaptive border display (ABD) designed to warn pilots of impending border infractions breaking rules of engagement. Two experiments were conducted under various levels of scenario complexities to test the ability of these displays to improve SA, reduce workload, and improve objective performance in a population of licensed civilian pilots. Study 1 results showed that the WEZ display significantly improved both performance and SA, and reduced workload. However, there were no significant effects of the ABD. In the second study, we used substantially more complex scenarios to investigate the effect of the ABD. Results showed that in these situations, the ABD had a significant effect on improving pilot performance. The results demonstrate the value of adaptive visualizations that highlight key aspects of the environment that impact performance. Although the results have verified the benefits of adaptive displays, more sensitive performance measures and diverse test pilots are suggested in the future to provide more confidence in applying these findings. Potential application of this research includes modern aircraft cockpit interface design under complex combat scenarios.

Cognitive Systems Engineering for Evolvable Human-in-the-Loop Data Fusion

Data fusion systems are increasingly being used to support military planning and decision making. Typically these systems are designed around the current capabilities of particular data collectors (e.g., sensors) and processing algorithms. They incorporate an ‘ontology’ that reflects the designers perception of the key features of the world (e.g., types of threats, classes of vehicles to be tracked) and how these can be parsed by the data fusion systems. As a consequence they are limited in their ability to adapt to the dynamic changes that inevitably arise in the operational environment (e.g., new sensors, weapons, tactics). This is representative of a more generic problem with current approaches to system design that result in rigid systems that are unable to evolve to keep pace with changing operational conditions. In this paper we present the results of an analysis, design, and development effort intended to move away from traditional data fusion systems towards evolvable human-in-the-loop data fusion systems. We discuss the analysis we conducted in support of an evolvable system design and provide an overview of the prototype evolvable data fusion system architecture we are developing.

Model-based measurement of situation awareness

Decision making in complex environments in the face of uncertain and missing information is a daunting task. We describe a modeling and simulation based approach to providing planners, analysts, and decision makers with a better understanding of the effect of imperfect information on the reliability of decisions made in such situations. We use techniques adopted from sensitivity analysis to evaluate the sensitivity of particular decision-making procedures to the uncertainty associated with the information that is being used to make the decision. We use this analysis to support the development of more robust decision-making procedures and effective and efficient information-gathering plans. We demonstrate how these tools can be used in both on-line decision analysis and off-line decision evaluation and development, and we describe how these tools can be used to support complex simulation systems such as the U.S. Armys Modeling Architecture for Technology and Research Experimentation (MATREX).

Designing an Adaptive Approach for the Real-Time Assessment and Augmentation of Performance of Cyber Analyst Teams

Full-spectrum cyber operations, including both Cyber Network Attack and Cyber Network Defense, place enormous cognitive demands on operators and teams. When demands are too high or tasks are not properly allocated, performance degrades, and missions may fail. A thorough, real-time evaluation of the state of the individual and the team would be an effective approach to avoiding operator overload. We describe an approach that supports the real-time assessment and augmentation of team performance. First, the physiological and affective state and the behavioral performance of individual operators is measured by fusing data from individual sensors. Signals from individual operators are then fused to enable a comprehensive and holistic characterization of team performance. Advanced modeling techniques are then implemented to compare current team performance with optimal levels of performance. Finally, augmentation strategies are recommended to optimize performance of cyber teams.

Adaptive Boundary Aids in Complex Air Combat Scenarios

Advances in aircraft operational capabilities have led to a dramatic increase in the operational tempo facing air combat aviators, which has in turn led to SA failures, particularly with respect to secondary information. For example, when engaging an air threat, aviators will often overlook key information such as geopolitical boundaries, resulting in potential infractions of rules of engagement. In a previous study, we investigated an Adaptive Border Display designed to maintain awareness of these boundaries, and found that it was not helpful. However, in that study, our scenarios did not create the high workload situations in which pilots lose track of these boundaries. In this study, we used significantly more complex scenarios to investigate this display, creating high workload situations for the aviators. Results showed that in these situations, the Adaptive Border display had a significant effect on improving aviator performance.

Hybrid Modeling of Cyber Adversary Behavior

Cyber adversaries continue to become more proficient and sophisticated, increasing the vulnerability of the network systems that pervade all aspects of our lives. While there are many approaches to modeling network behavior and identifying anomalous and potentially malicious traffic, most of these approaches detect attacks once they have already occurred, enabling reaction only after the damage has been done. In traditional security studies, mitigating attacks has been a focus of many research and planning efforts, leading to a rich field of adversarial modeling to represent and predict what an adversary might do. In this paper, we present an analogous approach to modeling cyber adversaries to gain a deeper understanding of the behavioral dynamics underlying cyber attacks and enable predictive analytics and proactive defensive planning. We present a hybrid modeling approach that combines aspects of cognitive modeling, decision-theory, and reactive planning to capture different facets of adversary decision making and behavior.