1 code implementation • NAACL 2022 • Rimvydas Rubavicius, Alex Lascarides
We present a procedure for learning to ground symbols from a sequence of stimuli consisting of an arbitrarily complex noun phrase (e. g. “all but one green square above both red circles.”) and its designation in the visual scene.
no code implementations • ACL (splurobonlp) 2021 • Mattias Appelgren, Alex Lascarides
This paper describes a method for learning from a teacher’s potentially unreliable corrective feedback in an interactive task learning setting.
1 code implementation • 26 Oct 2023 • Antonio Valerio Miceli-Barone, Alex Lascarides, Craig Innes
Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars.
1 code implementation • 11 Aug 2023 • Gautier Dagan, Frank Keller, Alex Lascarides
While Large Language Models (LLMs) can solve many NLP tasks in zero-shot settings, applications involving embodied agents remain problematic.
1 code implementation • 5 May 2023 • Jonghyuk Park, Alex Lascarides, Subramanian Ramamoorthy
Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users.
no code implementations • 7 Feb 2023 • Mattias Appelgren, Alex Lascarides
Some actions must be executed in different ways depending on the context.
1 code implementation • 27 Jan 2023 • Gautier Dagan, Frank Keller, Alex Lascarides
However, predicting the effects of an action before it is executed is crucial in planning, where coherent sequences of actions are often needed to achieve a goal.
no code implementations • 31 Jul 2019 • Yordan Hristov, Daniel Angelov, Michael Burke, Alex Lascarides, Subramanian Ramamoorthy
Learning from demonstration is an effective method for human users to instruct desired robot behaviour.
no code implementations • 27 Feb 2019 • 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.
no code implementations • 17 Jul 2018 • Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy
Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world.
no code implementations • 10 Jan 2018 • Craig Innes, Alex Lascarides, Stefano V. Albrecht, Subramanian Ramamoorthy, Benjamin Rosman
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 known in advance and does not change during learning.
no code implementations • WS 2017 • Yordan Hristov, Svetlin Penkov, Alex Lascarides, Subramanian Ramamoorthy
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 symbols in the physical world.
no code implementations • EACL 2017 • Simon Keizer, Markus Guhe, Heriberto Cuay{\'a}huitl, Ioannis Efstathiou, Klaus-Peter Engelbrecht, Mihai Dobre, Alex Lascarides, Oliver Lemon
In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {``}Settlers of Catan{''}.