Satisficing Trust in Human Robot Teams
Lead Research Organisation:
UNIVERSITY OF BIRMINGHAM
Department Name: School of Computer Science
Abstract
In this project, we design and develop Human-Robot Teams (using experiments with real robots and modelling with Reinforcement Learning) to conduct urban search and related activity. A team will consist of 1-3 human operators and 2-6 robots. We extend the definition of a 'team' beyond robots and humans on the ground. Drawing an analogy with the management of major incidents (in UK Emergency Services), operational activity is performed at the 'bronze' level, i.e., by the local human-robot team which is overseen by tactical coordinators at the 'silver' level, e.g., providing guidance on legal or other constraints, and which answers to high-level strategic command at 'gold' level, e.g., redefining goals for the mission etc. In this way the 'team' is more than local coordination and trust applies through the command hierarchy as well as horizontally across each level. Communication may be intermittent, and the mission's goals and constraints might change during the mission. This is a further driver of variation in trust, along with mission, activity, situation etc. Each team member, human or robot, will be allocated tasks within the team and perform these in an autonomous manner. Key to team performance will be the ability to acquire and maintain Distributed Situation Awareness, i.e., team members will have their own interpretation of the situation as they see it, and their own interpretation of the behaviour of their teammates. Teammate behaviour could be inferred from observation of what teammates are doing in a given situation, and whether this is to be expected. This creates behavioural markers of trust. We also consider the confidence with which teammates might express the Situation Awareness, e.g., in terms of their interpretation of the data they perceive in the situation. From the interpretation of teammate behaviour, we explore appropriately scaled trust (using the concept of a 'ladder of trust' on which trust moves up and down depending on the quality of situation awareness, the behaviour of teammates, the threat posed by the situation). From the Distributed Situation Awareness, we also explore counter-factual ('what-if') reasoning to cope with uncertain and ambiguous situations (where ambiguity might relate to permissions and rights to perform tasks, or to the consequences of an action, as well as Situation Awareness).
Publications
Hunt E
(2024)
Steps towards Satisficing Distributed Dynamic Team Trust
in Proceedings of the AAAI Symposium Series
Hunt,E.R.
(2023)
Steps towards Satisficing Distributed Dynamic Team Trust
Milivojevic S
(2024)
Swift Trust in Mobile Ad Hoc Human-Robot Teams
Segura M
(2025)
Opponent Shaping in LLM Agents
| Description | We demonstrate how 'trust' (held by people OR robots) within a human-robot team varies across a mission. We demonstrate behavioural markers of trust (to allow trust to be inferred from the actions of teammates rather than through self-report). We use a novel combination of virtual simulations to emulate the use of 'drones' in a fire-fighter search scenario. |
| Exploitation Route | We are hosting a meeting with Fire and Rescue Services. There has been interest in exploring our approach to simulation for application in Fire and Rescue Training. We are also in conversation with Security organisations. |
| Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Security and Diplomacy |
| Description | Use of video in Fire and Rescue |
| Organisation | London Fire Brigade |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Overview of approaches to coordinating drone data into command decision making |
| Collaborator Contribution | Our work on Distributed Situation Awareness and Behavioural Markers, particularly through sharing of video, led to discussions with London Fire and Rescue Service for their drone deployment and has resulted in secondment of Olly Sapsford as a part-time PhD student at University of Birmingham. He will be exploring ways to automatically extract (from personal radio and body-worn cameras) behavioural markers of command decision making in training exercises. |
| Impact | None |
| Start Year | 2025 |