The impact of expressing emotion within explainable AI in human-agent teamwork

With the increasing development of artificial intelligence (AI), there is a more significant opportunity for humans and agents to collaborate in teamwork. In Human-Agent Teamwork (HAT) settings, collaboration requires communication, and the agent displaying emotion can impact how human teammates communicate and work together with the agent. This...

Exploring the effect of the information amount in explanation on different gaming expertise levels

Explainable AI (XAI) has gained increasing attention from more and more researchers with an aim to improve human interaction with AI systems. In the context of human-agent teamwork (HAT), providing explainability to the agent helps to increase shared team knowledge and belief, therefore improving overall teamwork. With various backgrounds and...

The Influence of Interdependence Relationships on Trust Repair Strategies and Collaboration Fluency

Intelligent agents are increasingly required to engage in collaboration with humans in the context of human-agent teams (HATs) to achieve shared goals. Interdependence is a fundamental concept in teamwork. It enables humans and robots to leverage their capabilities and collaboratively work towards a shared goal, fostering the development of...

The Influence of Adapting an Agent’s Explanation Style to a Human Team Leader Role on Human-Agent Teamwork during a Simulated Search and Rescue Task

Communication is one of the main challenges in Human-Agent Teams (HATs). An important aspect of communication in HATs is the use of explanation styles. This thesis examines the influence of an explainable agent adapting its explanation style to a supervising human team leader on team performance, trust, situation awareness, collaborative fluency...

Fictional Co-Play for Human-Agent Collaboration: Evaluating state-of-the-art reinforcement learning technique for adaptability to human collaborators

A longstanding problem in the area of reinforcement learning is human-agent col- laboration. As past research indicates that RL agents undergo a distributional shift when they start collaborating with human beings, the goal is to create agents that can adapt. We build upon research using the two-player Overcooked environment to repro- duce a...

Using Human Workload to Adjust Agent Explanations in Human-agent Teamwork

Artificial intelligence systems assist humans in more and more cases. However, such systems' lack of explainability could lead to a bad performance of the teamwork, as humans might not cooperate with or trust systems with black-box algorithms opaque to them. This research attempts to improve the explainability of artificial intelligence systems...

The Impact of Tailoring Agent Explanations According to Human Performance on Human-AI Teamwork

Nowadays, artificial intelligence (AI) systems are embedded in many aspects of our lives more than ever before. Autonomous AI systems (agents) are aiding people in mundane daily tasks, even outperforming humans in several cases. However, agents still depend on humans in unexpected circumstances. Thus, the main goal of these agents has...

Assessing artificial trust in human-agent teams: A conceptual model

As intelligent agents are becoming human's teammates, not only do humans need to trust intelligent agents, but an intelligent agent should also be able to form artificial trust, i.e. a belief regarding human's trustworthiness. We see artificial trust as the beliefs of competence and willingness, and we study which internal factors (krypta) of...

Context-Sensitive Sharedness Criteria for Teamwork (Extended Abstract)

Teamwork between humans and intelligent systems gains importance with the maturing of agent and robot technology. In the social sciences, sharedness of mental models is used to explain and understand teamwork. To use this concept for developing teams that include agents, we propose contextsensitive sharedness criteria. These criteria specify how...

ePartners for Dynamic Task Allocation and Coordination

This dissertation promotes a focus shift from automation extending human capabilities to automation partnering with the human. Highly automated systems tend to be incompatible with humans, leading to degradations in performance that may lead to serious problems in high-risk professional domains, such as the Navy and Urban Search and Rescue. This...