RADAR: automated task planning for proactive decision support

Grover, Sachin; Sengupta, Sailik; Chakraborti, Tathagata; Ghosh, Sayantan; Kambhampati, Subbarao

doi:10.1080/07370024.2020.1726751

Cited by 20 publications

(31 citation statements)

References 42 publications

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“…However, recent work [68] has shown that achieving such behavior is computationally no harder than its classical planning counterpart! Furthermore, recognizing that plans are not made in vacuum but often in the context of interactions with end users, can lead to a more efficient planning process with explainable components than without, for example, in collaborative planning scenarios [29] or in anytime planners that can preserve high-level constraints in partial plans as it plans along [26]. As the XAIP community comes to terms with its own accuracy versus efficiency trade-offs, parallel to similar arguments in the XAI community at large, a whole new world of possibilities open up in imbuing established planning approaches with the latest and best XAIP-components.…”

Section: Discussionmentioning

confidence: 99%

“…-End user: This is the person who interacts with the system in the form of a user. For a planning system, this may be the human teammate in a human-robot team [13] who is impacted by, or is a direct stakeholder in the plans of the robot, or user collaborating with an automated planner in a decision support setting [29]. -Domain Designer: This is the person involved in the acquisition of the model that the system works with: e.g.…”

Section: The Many Faces Of Xaipmentioning

confidence: 99%

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The Emerging Landscape of Explainable Automated Planning & Decision Making

Chakraborti¹,

Sreedharan

Kambhampati

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years and contrast that with earlier efforts in the field in terms of techniques, target users, and delivery mechanisms. We hope that the survey will provide guidance to new researchers in automated planning towards the role of explanations in the effective design of human-in-the-loop systems, as well as provide the established researcher with some perspective on the evolution of the exciting world of explainable planning.

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Section: Discussionmentioning

confidence: 99%

Section: The Many Faces Of Xaipmentioning

confidence: 99%

The Emerging Landscape of Explainable Automated Planning & Decision Making

Chakraborti¹,

Sreedharan

Kambhampati

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

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“…Additionally, XAIP techniques have also been applied to plan-based decision support systems in efforts to improve human-in-the-loop planning. For example, RADAR by Grover et al provides XAIP features such as plan summarization, plan explanations in the form of minimally complete contrastive explanations, plan validation, and action and plan suggestions to improve decision making in time critical scenarios (Grover et al 2020). Valmeekam et al extend this RADAR and develop RADAR-X which leverages user queries understand user preferences for providing refined plan suggestions (Valmeekam et al 2020).…”

Section: Explainable Aimentioning

confidence: 99%

“…Table 1 provides example explanations of our E SB explanations in comparison to action-based suggestions, a IDS , that provide the next recommended action. Note, a IDS is most closely modelled after the actionbased suggestion feature in RADAR (Grover et al 2020).…”

Section: Generating Subgoal-based Explanationsmentioning

confidence: 99%

“…Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended to the user is always correct (Grover et al 2020;Valmeekam et al 2020). However, optimal IDS decision making cannot be guaranteed in complex real world deployments.…”

Section: Introductionmentioning

confidence: 99%

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Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems

Das¹,

Kim²,

Chernova³

2022

Preprint

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Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce incorrect output or fail to work altogether. The field of explainable AI planning (XAIP) has sought to develop techniques that make the decision making of sequential decision making AI systems more explainable to end-users. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always correct. In this work, we examine novice user interactions with a non-robust IDS system -one that occasionally recommends the wrong action, and one that may become unavailable after users have become accustomed to its guidance. We introduce a novel explanation type, subgoal-based explanations, for planning-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance, improve user ability to distinguish optimal and suboptimal IDS recommendations, are preferred by users, and enable more robust user performance in the case of IDS failure.

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Human‐aware AI —A foundational framework for human–AI interaction

Sreedharan

2023

AI Magazine

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We are living through a revolutionary moment in AI history. Users from diverse walks of life are adopting and using AI systems for their everyday use cases at a pace that has never been seen before. However, with this proliferation, there is also a growing recognition that many of the central open problems within AI are connected to how the user interacts with these systems. To name two prominent examples, consider the problems of explainability and value alignment. Each problem has received considerable attention within the wider AI community, and much promising progress has been made in addressing each of these individual problems. However, each of these problems tends to be studied in isolation, using very different theoretical frameworks, while a closer look at each easily reveals striking similarities between the two problems. In this article, I wish to discuss the framework of human‐aware AI (HAAI) that aims to provide a unified formal framework to understand and evaluate human–AI interaction. We will see how this framework can be used to both understand explainability and value alignment and how the framework also lays out potential novel avenues to address these problems.

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RADAR: automated task planning for proactive decision support

Cited by 20 publications

References 42 publications

The Emerging Landscape of Explainable Automated Planning & Decision Making

The Emerging Landscape of Explainable Automated Planning & Decision Making

Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems

Human‐aware AI —A foundational framework for human–AI interaction

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