TY - GEN
T1 - Explainability in multi-agent path/motion planning
T2 - International Conference on Autonomous Agents and Multiagent Systems 2022
AU - Brandao, Martim
AU - Mansouri, Masoumeh
AU - Mohammed, Areeb
AU - Luff, Paul
AU - Coles, Amanda
PY - 2022/5/9
Y1 - 2022/5/9
N2 - Multi-Agent Path Finding (MAPF) and Multi-Robot Motion Planning (MRMP) are complex problems to solve, analyze and build algorithms for. Automatically-generated explanations of algorithm output, by improving human understanding of the underlying problems and algorithms, could thus lead to better user experience, developer knowledge, and MAPF/MRMP algorithm designs. Explanations are contextual, however, and thus developers need a good understanding of the questions that can be asked about algorithm output, the kinds of explanations that exist, and the potential users and uses of explanations in MAPF/MRMP applications. In this paper we provide a first step towards establishing a taxonomy of explanations, and a list of requirements for the development of explainable MAPF/MRMP planners. We use interviews and a questionnaire with expert developers and industry practitioners to identify the kinds of questions, explanations, users, uses, and requirements of explanations that should be considered in the design of such explainable planners. Our insights cover a diverse set of applications: warehouse automation, computer games, and mining.
AB - Multi-Agent Path Finding (MAPF) and Multi-Robot Motion Planning (MRMP) are complex problems to solve, analyze and build algorithms for. Automatically-generated explanations of algorithm output, by improving human understanding of the underlying problems and algorithms, could thus lead to better user experience, developer knowledge, and MAPF/MRMP algorithm designs. Explanations are contextual, however, and thus developers need a good understanding of the questions that can be asked about algorithm output, the kinds of explanations that exist, and the potential users and uses of explanations in MAPF/MRMP applications. In this paper we provide a first step towards establishing a taxonomy of explanations, and a list of requirements for the development of explainable MAPF/MRMP planners. We use interviews and a questionnaire with expert developers and industry practitioners to identify the kinds of questions, explanations, users, uses, and requirements of explanations that should be considered in the design of such explainable planners. Our insights cover a diverse set of applications: warehouse automation, computer games, and mining.
KW - multi-agent path finding
KW - multi-robot motion planning
KW - explainable planning
KW - explainable AI
UR - https://aamas2022-conference.auckland.ac.nz/
U2 - 10.5555/3535850.3535871
DO - 10.5555/3535850.3535871
M3 - Conference contribution
SN - 9781450392136
T3 - AAMAS: International Conference on Autonomous Agents and Multiagent Systems
SP - 172
EP - 180
BT - AAMAS '22
PB - International Foundation for Autonomous Agents and Multiagent Systems
Y2 - 9 May 2022 through 13 May 2022
ER -