Explainable Planning

By Maria Fox, Derek Long, Daniele Magazzeni. 2017

305 citations recorded by the source corpus.

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 modelbased representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, AI Ethics, Fairness & Society

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Citations in the corpus

Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, background): “Explainable planning [77] is an emerging discipline that exploits the model-based representations that exist in the planning community.”
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, neutral): “[170].”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “[46] who have similar questions in planning, such as “Why didn’t you do something else (that I would have done)?”.”
Towards Explainable Shared Control using Augmented Reality (2019, background): “Model-based intention estimation [4] and arbitration algorithms make this an easier endeavour [12].”
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations (2018, background): “There is increasing interest within the automated planning community to solve the problem of generating explanations for plans ([Fox, Long, and Magazzeni, 2017]).”
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation (2018, background): “It is generally agreed upon that the goal of XAI is to increase users’ trust, their ability to interact with the systems and with their decisions, and improve the transparency of the system [13].”
Balancing Explicability and Explanation in Human-Aware Planning (2017, background): “forthe plans of humans cohabiting its workspace, to “explicable planning” [14, 23, 34, 35] where a robot generates plans that are explicable or predictable to a human observer, to “plan explanations” [10, 17, 24] where the agent uses explanations to bring the human (who may have a different understa”
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, background): “Explainable planning [77] is an emerging discipline that exploits the model-based representations that exist in the planning community.”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “and battlefield simulations [12], just to mention a few.”