Web Planner: A Tool to Develop, Visualize, and Test Classical Planning Domains

Magnaguagno, Maurício Cecílio; Pereira, Ramon Fraga; More, Martin D.; Meneguzzi, Felipe

doi:10.1007/978-3-030-38561-3_11

Cited by 10 publications

(14 citation statements)

References 16 publications

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“…Members of the A 3 group have also generated a number of contributions in automated planning modelling techniques and formalism extensions. First, Maguaguagno, Meneguzzi, and others [54,55] developed a series of tools for developing, testing, and visualising planning domain descriptions for classical planning. These tools have been recognised not only as useful pedagogical tools, but also as informative mechanisms for visualising heuristic informativeness based on the design of each planning domain.…”

Section: Automated Planning and Synthesismentioning

confidence: 99%

Resilience, reliability, and coordination in autonomous multi-agent systems

Cardoso

Logan

Meneguzzi

et al. 2022

AIC

Self Cite

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Multi-agent systems is an evolving discipline that encompasses many different branches of research. The long-standing Agents at Aberdeen ( A 3 ) group undertakes research across several areas of multi-agent systems, focusing in particular on aspects related to resilience, reliability, and coordination. In this article we introduce the group and highlight past research successes in those themes, building a picture of the strengths within the group. We close the paper outlining the future direction of the group and identify key open challenges and our vision towards solving them.

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Section: Automated Planning and Synthesismentioning

confidence: 99%

Resilience, reliability, and coordination in autonomous multi-agent systems

Cardoso

Logan

Meneguzzi

et al. 2022

AIC

Self Cite

View full text Add to dashboard Cite

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“…There has also been some effort by the planning and scheduling community to create user interfaces for planning and scheduling problems (Freedman et al 2018). While some work aims to show users the space of alternate plans (Gopalakrishnan and Kambhampati 2018; Magnaguagno et al 2020;Chakraborti et al 2018), others aim to create systems to aid users in the creation of plans (e.g., Planimation (Chen et al 2020)) or for assistance with domain modeling (e.g., Conductor (Bryce et al 2017)). 5 These kinds of systems are essential steps towards the creation of a unified planning interface, especially when humans are involved in the loop.…”

Section: Related Workmentioning

confidence: 99%

“…Simple classical domains like BlocksWorld and Logistics naturally lend themselves to the physical space, presenting users the ability to keep track of the current state of the world by tracking their positions in their mental space. Moreover, planning visualization interfaces like Planimation (Chen et al 2020) and WebPlanner (Magnaguagno et al 2020) utilize state-space visualizations to assist in planning and display plan execution. Planimation, in particular, allows users to create visualizations for plans using an animation profile to specify how different elements are visualized.…”

Section: State-space Visualization: Abstractionmentioning

confidence: 99%

VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems

Kumar

Vasileiou

Bancilhon

et al. 2022

ICAPS

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Advancements in explanation generation for automated planning algorithms have moved us a step closer towards realizing the full potential of human-AI collaboration in real-world planning applications. Within this context, a framework called model reconciliation has gained a lot of traction, mostly due to its deep connection with a popular theory in human psychology, known as the theory of mind. Existing literature in this setting, however, has mostly been constrained to algorithmic contributions for generating explanations. To the best of our knowledge, there has been very little work on how to effectively convey such explanations to human users, a critical component in human-AI collaboration systems. In this paper, we set out to explore to what extent visualizations are an effective candidate for conveying explanations in a way that can be easily understood. Particularly, by drawing inspiration from work done in visualization systems for classical planning, we propose a visualization framework for visualizing explanations generated from model reconciliation algorithms. We demonstrate the efficacy of our proposed system in a comprehensive user study, where we compare our framework against a text-based baseline for two types of explanations – domain-based and problem-based explanations. Results from the user study show that users, on average, understood explanations better when they are conveyed via our visualization system compared to when they are conveyed via a text-based baseline.

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“…Efforts have gone into the generation and communication of explanations for complex systems. Some of the approaches proposed are algorithm-based [2], that is, they investigate the underlying algorithm in detail in order to support debugging tasks [7]. These types of explanations are particularly tailored for specific algorithms (algorithm dependent) and the explainer is usually intrinsic.…”

Section: Related Workmentioning

confidence: 99%

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles’ Actions

Omeiza

Anjomshoae

Webb

et al. 2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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Commentary driving is a technique in which drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, and thereby assist a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While collecting sensor data of the vehicle's surroundings, we obtained driving commentary from a driving instructor using the think-aloud protocol. We analysed the driving commentary and uncovered an explanation style; the driver first announces his observations, announces his plans, and then makes general remarks. He also made counterfactual comments. We successfully demonstrated how factual and counterfactual natural language explanations that follow this style could be automatically generated using a simple tree-based approach. Generated explanations for longitudinal actions (e.g., stop and move) were deemed more intelligible and plausible by human judges compared to lateral actions, such as lane changes. We discussed how our approach can be built on in the future to realise more robust and effective explainability for driver assistance as well as partial and conditional automation of driving functions.

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Web Planner: A Tool to Develop, Visualize, and Test Classical Planning Domains

Cited by 10 publications

References 16 publications

Resilience, reliability, and coordination in autonomous multi-agent systems

Resilience, reliability, and coordination in autonomous multi-agent systems

VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles’ Actions

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