Explainable Planning

Abstract

As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems cooperate in problem-solving, but also it is a necessary step in the process of building trust as humans migrate greater responsibility to such systems. The challenge is to find effective ways to communicate the foundations of AI-driven behaviour, when the algorithms that drive it are far from transparent to humans. In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.

Presented at the IJCAI-17 workshop on Explainable AI (http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/). Melbourne, August 2017

Cited by in corpus (9)

• What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
• Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
• Desiderata for Planning Systems in General-Purpose Service Robots
• Iterative Planning with Plan-Space Explanations: A Tool and User Study
• A Game-Based Approach for Helping Designers Learn Machine Learning Concepts
• Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence
• Encoding Compositionality in Classical Planning Solutions
• Argument Schemes and Dialogue for Explainable Planning
• Provenance-Based Assessment of Plans in Context