Jason M. Bindewald

A function-to-task process model for adaptive automation system design

Abstract Adaptive automation systems allow the user to complete a task seamlessly with a computer performing tasks at which the human operator struggles. Unlike traditional systems that allocate functions to either the human or the machine, adaptive automation varies the allocation of functions during system operation. Creating these systems requires designers to consider issues not present during static system development. To assist in adaptive automation system design, this paper presents the concept of inherent tasks and takes advantage of this concept to create the function-to-task design process model . This process model helps the designer determine how to allocate functions to the human, machine, or dynamically between the two. An illustration of the process demonstrates the potential complexity within adaptive automation systems and how the process model aids in understanding this complexity during early stage design.

Timing within human-agent interaction and its effects on team performance and human behavior

Current systems incorporating human-agent interaction typically place the human in a supervisory role and the agent as a subordinate. However, a key aspect of teaming is the dynamic shift in roles. Depending on the situation at hand, teaming could lead to a peer relationship where the human and agent are working together on the same task. This research investigates how the timing of agent actions impacts team performance, as well as human workload and behavior. A human-in-the-loop experiment demonstrated that when the agent performs tasks faster than the human, the human tends to become reliant upon the automation and assumes a supervisory role. A human performance model predicts that extending agent execution time will decrease human reliance on the automation. However, in the environment under investigation, a tradeoff exists between team performance and human involvement.

Trajectory Generation with Player Modeling

The ability to perform tasks similarly to how a specific human would perform them is valuable in future automation efforts across several areas. This paper presents a \(k\)-nearest neighbor trajectory generation methodology that creates trajectories similar to those of a given user in the Space Navigator environment using cluster-based player modeling. This method improves on past efforts by generating trajectories as whole entities rather than creating them point-by-point. Additionally, the player modeling approach improves on past human trajectory modeling efforts by achieving similarity to specific human players rather than general human-like game-play. Results demonstrate that player modeling significantly improves the ability of a trajectory generation system to imitate a given user’s actual performance.

Clustering-Based Online Player Modeling

Being able to imitate individual players in a game can benefit game development by providing a means to create a variety of autonomous agents and aid understanding of which aspects of game states influence game-play. This paper presents a clustering and locally weighted regression method for modeling and imitating individual players. The algorithm first learns a generic player cluster model that is updated online to capture an individual’s game-play tendencies. The models can then be used to play the game or for analysis to identify how different players react to separate aspects of game states. The method is demonstrated on a tablet-based trajectory generation game called Space Navigator.

Enabling Bluetooth Low Energy auditing through synchronized tracking of multiple connections

Abstract Bluetooth Low Energy is a wireless communications protocol that is increasingly used in critical infrastructure applications, especially for inter-sensor communications in wireless sensor networks. Recent security research notes a trend in which developers and vendors have opted out of implementing Bluetooth Low Energy link security in many devices, enabling protocol attacks and attack frameworks. To help defend devices with no link security, researchers recommend the use of Bluetooth Low Energy traffic sniffers to generate auditable communications logs. Unfortunately, current sniffers can only follow a single connection at a time, and some are ineffective at capturing long-lived connections due to synchronization problems. These limitations make current sniffers impractical for use in wireless sensor networks. This paper presents Bluetooth Low Energy Multi (BLE-Multi), a firmware enhancement to the open-source Ubertooth One that enables the sniffing of multiple simultaneous long-lived connections. To increase the capture effectiveness for long-lived connections, a novel synchronization mechanism is proposed that uses transmissions of empty packets to infer information about connection timing. Multi-connection sniffing is achieved by opportunistically switching between connections as they move from the active to inactive state, which is an inherent function in Bluetooth Low Energy to help conserve energy. The experimental evaluations demonstrate that BLE-Multi simultaneously captures multiple active connections while outperforming Ubertooth One when it captures a single connection, paving the way for the development and implementation of automated defensive tools for Bluetooth Low Energy and wireless sensor networks.

Measuring Human Trust Behavior in Human-Machine Teams

This paper presents a paradigm for distilling trust behaviors in human-machine teams. The paradigm moves beyond diagnostic alarms-based automation definitions of compliance and reliance toward a view of trust behavior that includes automations where the machine has authority to act on behalf of the human-machine team in the environment. The paradigm first determines the purpose of the automation and then relies on three types of authority within the human-machine team to identify what trust behaviors will look like in specific instances. An example using the Space Navigator environment demonstrates how trust behaviors can be measured.

Effects of agent timing on the human-agent team

Abstract As technology becomes more sophisticated, autonomous agents are applied more frequently to improve system performance. The current research employed a five step method, including modeling, simulation, and human experimentation to explore the effect of an artificial agent’s timing on the performance of a human-agent team within a highly dynamic task environment. Agent timing significantly influenced the role assumed by the human within the team. Further, agent timing changed system performance by approximately 40% within the experimental conditions. Results indicate that an artificial agent’s timing can be varied as a function of the task demands placed upon the human-agent team to maintain an appropriate level of human activity and engagement. Therefore, agent timing may be controlled to adapt autonomy to provide an apparent continuum along which to control human engagement in systems employing human-agent teaming within dynamic environments.

SECURING BLUETOOTH LOW ENERGY LOCKS FROM UNAUTHORIZEDACCESS AND SURVEILLANCE

This chapter describes several vulnerabilities that affect commercial and residential Bluetooth Low Energy security devices and outlines methods for exploiting plaintext, obfuscated and hard-coded passwords, brute forcing passwords and hashes, fuzzing commands and performing man-in-the-middle attacks. Evaluations reveal that 75% of the tested security and access control systems have vulnerabilities that grant unauthorized access. In addition to obtaining access, malicious actors can extract sensitive information that can be used to develop patterns of human behavior. This chapter discusses five solutions for preventing or mitigating Bluetooth Low Energy security breaches, most of which involve minimal implementation overhead on the part of developers.

Relationships between User Demographics and User Trust in an Autonomous Agent

Reliability of autonomous agents has been shown to play a pivotal role in the human-agent team. This research investigates the relationship between demographic factors and trust using the application environment, Space Navigator. Using stepwise multiple linear regression, it was found that workload (NASA-TLX), gender, education level, and the reliability of the autonomous agent impact the perceived reliability or user trust in the system. When the user experienced higher workload, the user placed less trust in the autonomous agent. Females trusted the agent less and more educated users trusted the autonomous agent more. Finally, more reliable agents led to higher levels of trust in the agent by human users.

Designing an Automated Agent to Encourage Human Reliance

Human reliance on automated agents can be critically important, as exemplified by a pilot relying on an automated ground collision avoidance system. While it is important that the automated agent perform a task well, thus promoting reliance on the automation, it is difficult to test human reliance on automated agents in safety-critical systems. This paper presents an automated agent designed to enable testing of human reliance on automation in the Space Navigator environment. The automated agent performs collision detection and avoidance tasks in the environment, aiding the human participant in real-time. We present a collision detection and avoidance model, comparing three potential methods for collision avoidance. Analysis shows that the new agent’s performance when teamed with another simulated agent improves upon previous individual human and human-agent team performances in the same environment, thus making it logical for humans to rely upon it. A human-subjects study confirms that the resulting automated agent/environment pairing enables human reliance studies in a low-states automation environment.