Actor-Focused Interactive Visualization for AI Planning


  • Gabriel Dias Cantareira King's College London
  • Gerard Canal King's College London
  • Rita Borgo King's College London



XAIP, Planning Visualization, Visual Analytics, Robotics


As we grow more reliant on AI systems for an increasing variety of applications in our lives, the need to understand and interpret such systems also becomes more pronounced, be it for improvement, trust, or legal liability. AI Planning is one type of task that provides explanation challenges, particularly due to the increasing complexity in generated plans and convoluted causal chains that connect actions and determine overall plan structure. While there are many recent techniques to support plan explanation, visual aids for navigating this data are quite limited. Furthermore, there is often a barrier between techniques focused on abstract planning concepts and domain-related explanations. In this paper, we present a visual analytics tool to support plan summarization and interaction, focusing in robotics domains using an actor-based structure. We show how users can quickly grasp vital information about actions involved in a plan and how they relate to each other. Finally, we present a framework used to design our tool, highlighting how general PDDL elements can be converted into visual representations and further connecting concept to domain.




How to Cite

Cantareira, G. D., Canal, G., & Borgo, R. (2022). Actor-Focused Interactive Visualization for AI Planning. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 678-686.