Search Results for author: Ronald P. A. Petrick

Found 8 papers, 0 papers with code

Temporal Planning with Incomplete Knowledge and Perceptual Information

no code implementations20 Jul 2022 Yaniel Carreno, Yvan Petillot, Ronald P. A. Petrick

In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital.

Actions You Can Handle: Dependent Types for AI Plans

no code implementations24 May 2021 Alasdair Hill, Ekaterina Komendantskaya, Matthew L. Daggitt, Ronald P. A. Petrick

Verification of AI is a challenge that has engineering, algorithmic and programming language components.

Investigating Human Response, Behaviour, and Preference in Joint-Task Interaction

no code implementations27 Nov 2020 Alan Lindsay, Bart Craenen, Sara Dalzel-Job, Robin L. Hill, Ronald P. A. Petrick

Our intention is that these lessons can inform the design of interaction agents -- including those using planning techniques -- whose behaviour is conditioned on the user's response, including affective measures of the user (i. e., explicitly incorporating the user's affective state within the planning model).

Towards Social HRI for Improving Children's Healthcare Experiences

no code implementations9 Oct 2020 Mary Ellen Foster, Ronald P. A. Petrick

This paper describes a new research project that aims to develop a social robot designed to help children cope with painful and distressing medical procedures in a clinical setting.

Proof-Carrying Plans: a Resource Logic for AI Planning

no code implementations10 Aug 2020 Alasdair Hill, Ekaterina Komendantskaya, Ronald P. A. Petrick

In this paper, we present a novel resource logic, the Proof Carrying Plans (PCP) logic that can be used to verify plans produced by AI planners.

Affordances in Robotic Tasks -- A Survey

no code implementations15 Apr 2020 Paola Ardón, Èric Pairet, Katrin S. Lohan, Subramanian Ramamoorthy, Ronald P. A. Petrick

Affordances are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects.

Robotics

Learning Grasp Affordance Reasoning through Semantic Relations

no code implementations24 Jun 2019 Paola Ardón, Èric Pairet, Ronald P. A. Petrick, Subramanian Ramamoorthy, Katrin S. Lohan

We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object.

Robotics

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