4 citations · 13 across the 15 of their papers we have counts for
16 papers
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Yang Chen, Xiao Lin, Bo Yan +4
Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games…
A Community-driven vision for a new Knowledge Resource for AI
Vinay K Chaudhri, Chaitan Baru, Brandon Bennett +29
The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge re…
Efficient size-prescribed -core search
Yiping Liu, Bo Yan, Bo Zhao +3
-core is a subgraph where every node has at least neighbors within the subgraph. The -core subgraphs has been employed in large platforms like Network Repository to compr…
Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Gaël Gendron, Yang Chen, Mitchell Rogers +11
Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is ess…
Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?
Tim Hartill, Joshua Bensemann, Michael Witbrock +1
A distinction is often drawn between a model's ability to predict a label for an evaluation sample that is directly memorised from highly similar training samples versus an ability…
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Qiming Bao, Gael Gendron, Alex Yuxuan Peng +5
Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-li…
Meerkat Behaviour Recognition Dataset
Mitchell Rogers, Gaël Gendron, David Arturo Soriano Valdez +10
Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world. Current methods for documenting animal behaviou…
Neuromodulation Gated Transformer
Kobe Knowles, Joshua Bensemann, Diana Benavides-Prado +4
We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect. We…
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng +10
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical re…
Learning Density-Based Correlated Equilibria for Markov Games
Libo Zhang, Yang Chen, Toru Takisaka +3
Correlated Equilibrium (CE) is a well-established solution concept that captures coordination among agents and enjoys good algorithmic properties. In real-world multi-agent systems…
AbductionRules: Training Transformers to Explain Unexpected Inputs
Nathan Young, Qiming Bao, Joshua Bensemann +1
Transformers have recently been shown to be capable of reliably performing logical reasoning over facts and rules expressed in natural language, but abductive reasoning - inference…
Relating Blindsight and AI: A Review
Joshua Bensemann, Qiming Bao, Gaël Gendron +2
Processes occurring in brains, a.k.a. biological neural networks, can and have been modeled within artificial neural network architectures. Due to this, we have conducted a review…
DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions
Lin Ni, Qiming Bao, Xiaoxuan Li +5
Automated question quality rating (AQQR) aims to evaluate question quality through computational means, thereby addressing emerging challenges in online learnersourced question rep…
Learning to Guide a Saturation-Based Theorem Prover
Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni +8
Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the…
Adversarial Inverse Reinforcement Learning for Mean Field Games
Yang Chen, Libo Zhang, Jiamou Liu +1
Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems by leveraging mean field theory to simplify interactions among ag…
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni +7
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to o…