no code implementations • 18 Oct 2022 • Alan Lindsay, Andres Ramirez-Duque, Ronald P. A. Petrick, Mary Ellen Foster
We present an ongoing project that aims to develop a social robot to help children cope with painful and distressing medical procedures in a clinical setting.
no code implementations • 20 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.
no code implementations • 24 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.
no code implementations • 14 May 2021 • Paola Ardón, Èric Pairet, Katrin S. Lohan, Subramanian Ramamoorthy, Ronald P. A. Petrick
Affordances describe the possibilities for an agent to perform actions with an object.
no code implementations • 27 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).
no code implementations • 9 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.
no code implementations • 10 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.
no code implementations • 15 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
no code implementations • 24 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
