Planning in Robotics

Ghallab, Malik; Nau, Dana; Traverso, Paolo

doi:10.1016/b978-155860856-6/50029-6

Cited by 11 publications

(11 citation statements)

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“…Formally, these can be seen as states, and transitions which lead from a start state to a goal state (Ghallab et al, 2004). Often these plans are very difficult for humans to understand.…”

Section: Introductionmentioning

confidence: 99%

Effects of Individual Differences in Working Memory on Plan Presentational Choices

Tintarev

Masthoff

2016

Front. Psychol.

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This paper addresses research questions that are central to the area of visualization interfaces for decision support: (RQ1) whether individual user differences in working memory should be considered when choosing how to present visualizations; (RQ2) how to present the visualization to support effective decision making and processing; and (RQ3) how to evaluate the effectiveness of presentational choices. These questions are addressed in the context of presenting plans, or sequences of actions, to users. The experiments are conducted in several domains, and the findings are relevant to applications such as semi-autonomous systems in logistics. That is, scenarios that require the attention of humans who are likely to be interrupted, and require good performance but are not time critical. Following a literature review of different types of individual differences in users that have been found to affect the effectiveness of presentational choices, we consider specifically the influence of individuals' working memory (RQ1). The review also considers metrics used to evaluate presentational choices, and types of presentational choices considered. As for presentational choices (RQ2), we consider a number of variants including interactivity, aggregation, layout, and emphasis. Finally, to evaluate the effectiveness of plan presentational choices (RQ3) we adopt a layered-evaluation approach and measure performance in a dual task paradigm, involving both task interleaving and evaluation of situational awareness. This novel methodology for evaluating visualizations is employed in a series of experiments investigating presentational choices for a plan. A key finding is that emphasizing steps (by highlighting borders) can improve effectiveness on a primary task, but only when controlling for individual variation in working memory.

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“…Formally, these can be seen as states, and transitions which lead from a start state to a goal state (Ghallab et al, 2004). Often these plans are very difficult for humans to understand.…”

Section: Introductionmentioning

confidence: 99%

Effects of Individual Differences in Working Memory on Plan Presentational Choices

Tintarev

Masthoff

2016

Front. Psychol.

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“…Automated Planning with PDDL Automated planning is a decision-making task that involves reasoning about the sequence of actions (a plan) that achieves a set of goals (Ghallab, Nau, and Traverso 2004;Haslum et al 2019). A planning problem Π can be thought of as a tuple Π = ⟨P, A, I, G⟩, where P is a set of properties that define a state space (including possibly a set of objects), A is a set of actions, I is a set of initial state properties, and G is the set of goal conditions to be achieved.…”

Section: Background and Related Workmentioning

confidence: 99%

Planning Domain Simulation: An Interactive System for Plan Visualisation

De Pellegrin,

Petrick

2024

ICAPS

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Representing and manipulating domain knowledge is essential for developing systems that can visualize plans. This paper presents a novel plan visualisation system called Planning Domain Simulation (PDSim) that employs knowledge representation and manipulation techniques to support the plan visualization process. PDSim can use PDDL or the Unified Planning Library's Python representation as the underlying language for modelling planning problems and provides an interface for users to manipulate this representation through interaction with the Unity game engine and a set of planners. The system’s features include visualising plan components, and their relationships, identifying plan conflicts, and examples applied to real-world problems. The benefits and limitations of PDSim are also discussed, highlighting future research directions in the area.

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“…Traditional models of opinion dynamics often rely on broad strategies without considering the intricate interplay of individual nodes within a network. In contrast, Automated Planning offers a granular approach, devising specific sequences of actions tailored to transition a system from its current state to a desired state (Ghallab, Nau, and Traverso 2004). In the context of opinion dynamics, this could mean formulating targeted strategies to sway the collective opinion of a network in a more positive or negative direction, taking into account the unique characteristics and dependencies of each network node.…”

Section: Introductionmentioning

confidence: 99%

Expressive and Flexible Simulation of Information Spread Strategies in Social Networks Using Planning

Muppasani,

Narayanan,

Srivastava

et al. 2024

AAAI

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In the digital age, understanding the dynamics of information spread and opinion formation within networks is paramount. This research introduces an innovative framework that combines the principles of opinion dynamics with the strategic capabilities of Automated Planning. We have developed, to the best of our knowledge, the first-ever numeric PDDL tailored for opinion dynamics. Our tool empowers users to visualize intricate networks, simulate the evolution of opinions, and strategically influence that evolution to achieve specific outcomes. By harnessing Automated Planning techniques, our framework offers a nuanced approach to devise sequences of actions tailored to transition a network from its current opinion landscape to a desired state. This holistic approach provides insights into the intricate interplay of individual nodes within a network and paves the way for targeted interventions. Furthermore, the tool facilitates human-AI collaboration, enabling users to not only understand information spread but also devise practical strategies to mitigate potential harmful outcomes arising from it. Demo Video link - https://tinyurl.com/3k7bp99h

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Planning in Robotics

Cited by 11 publications

References 0 publications

Effects of Individual Differences in Working Memory on Plan Presentational Choices

Effects of Individual Differences in Working Memory on Plan Presentational Choices

Planning Domain Simulation: An Interactive System for Plan Visualisation

Expressive and Flexible Simulation of Information Spread Strategies in Social Networks Using Planning

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