77 citations · 101 across the 18 of their papers we have counts for
7 papers · 1 filter
Learning to Assemble Novel Structures with Unfamiliar Parts under Semantic Constraints
Jonghyuk Park, Alex Lascarides, Subramanian Ramamoorthy
This paper describes a neurosymbolic architecture for learning to assemble novel structures using evidence from embodied conversations and task demonstrations. We focus on scenario…
: How to learn from procedural How-to questions
Gautier Dagan, Frank Keller, Alex Lascarides
An agent facing a planning problem can use answers to how-to questions to reduce uncertainty and fill knowledge gaps, helping it solve both current and future tasks. However, their…
Learning Visually Grounded Domain Ontologies via Embodied Conversation and Explanation
Jonghyuk Park, Alex Lascarides, Subramanian Ramamoorthy
In this paper, we offer a learning framework in which the agent's knowledge gaps are overcome through corrective feedback from a teacher whenever the agent explains its (incorrect)…
Dialogue-based generation of self-driving simulation scenarios using Large Language Models
Antonio Valerio Miceli-Barone, Alex Lascarides, Craig Innes
Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars. Current simulation frameworks are driven by highly-specialist domain specific lang…
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…
Grounding Symbols in Multi-Modal Instructions
Yordan Hristov, Svetlin Penkov, Alex Lascarides +1
As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground sym…