In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital.
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).
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.
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 are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects.
We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object.