In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …

Neural encoder-decoder models for language generation can be trained to predict words
directly from linguistic or non-linguistic inputs. When generating with these so-called end-to …

This article presents the problem of distributed training with a decentralized execution policy
as a safe, optimal formation control for a heterogeneous nonlinear multi-agent system. The …

This paper presents CoLLIE: a simple, yet effective model for continual learning of how
language is grounded in vision. Given a pre-trained multimodal embedding model, where …

Situational context is crucial for linguistic reference to visible objects, since the same
description can refer unambiguously to an object in one context but be ambiguous or …

The ability for variation in language use is necessary for speakers to achieve their
conversational goals, for instance when referring to objects in visual environments. We …

The shift to neural models in Referring Expression Generation (REG) has enabled more
natural set-ups, but at the cost of interpretability. We argue that integrating pragmatic …

This paper proposes a novel task on commonsense-enhanced task-based dialogue
grounded in documents and describes the Task2Dial dataset, a novel dataset of document …

We propose an approach to referring expression generation (REG) in visually grounded
dialogue that is meant to produce referring expressions (REs) that are both discriminative …

This research introduces a novel approach to crisis management by implementing a multi-
agent algorithm within a strategic decision system. The proposed system harnesses multiple …