1 citations · 1 across the 3 of their papers we have counts for
5 papers
Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications
Craig Innes, Yordan Hristov, Georgios Kamaras +1
This paper presents an extension to the probabilistic programming language ProbRobScene, allowing users to automatically synthesize uniform experiment designs directly from environ…
ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments
Craig Innes, Subramanian Ramamoorthy
Robotic control tasks are often first run in simulation for the purposes of verification, debugging and data augmentation. Many methods exist to specify what task a robot must comp…
Elaborating on Learned Demonstrations with Temporal Logic Specifications
Craig Innes, Subramanian Ramamoorthy
Most current methods for learning from demonstrations assume that those demonstrations alone are sufficient to learn the underlying task. This is often untrue, especially if extra…
Learning Factored Markov Decision Processes with Unawareness
Craig Innes, Alex Lascarides
Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance. This assumption is sometimes unte…
Reasoning about Unforeseen Possibilities During Policy Learning
Craig Innes, Alex Lascarides, Stefano V Albrecht +2
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is…