870 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- City St George's, University of LondonGB152 papers
- University of OxfordGB100 papers
- Nankai UniversityCN81 papers
- Northampton Community CollegeUS36 papers
- Imperial College LondonGB35 papers
- Centre National de la Recherche ScientifiqueFR31 papers
- King's College LondonGB24 papers
- London Institute for Mathematical SciencesGB22 papers
- University College LondonGB21 papers
- University of LondonGB19 papers
- Royal Institution of Great BritainGB18 papers
- National Institute for Theoretical PhysicsZA17 papers
20 papers · 1 filter
A representational framework for learning and encoding structurally enriched trajectories in complex agent environments
Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso
The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address t…
Algebras of actions in an agent's representations of the world
Alexander Dean, Eduardo Alonso, Esther Mondragon
In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reprodu…
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
Jiaoyan Chen, Hang Dong, Janna Hastings +6
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.…
Proceedings 39th International Conference on Logic Programming
Enrico Pontelli, Stefania Costantini, Carmine Dodaro +7
This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 t…
Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
Erik B. Myklebust, Ernesto Jiménez-Ruiz, Jiaoyan Chen +2
We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment…
Logic Tensor Networks
Samy Badreddine, Artur d'Avila Garcez, Luciano Serafini +1
Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-…
OWL2Vec*: Embedding of OWL Ontologies
Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz +3
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and…
Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles
Xingyu Zhao, Kizito Salako, Lorenzo Strigini +2
Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular…
Layerwise Knowledge Extraction from Deep Convolutional Networks
Simon Odense, Artur d'Avila Garcez
Knowledge extraction is used to convert neural networks into symbolic descriptions with the objective of producing more comprehensible learning models. The central challenge is to…
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Luis C. Lamb, Artur Garcez, Marco Gori +3
Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relationa…
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Ernesto Jiménez-Ruiz, Asan Agibetov, Jiaoyan Chen +2
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic…
On the Sensory Commutativity of Action Sequences for Embodied Agents
Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Perception of artificial agents is one the grand challenges of AI research. Deep Learning and data-driven approaches are successful on constrained problems where perception can be…
Correcting Knowledge Base Assertions
Jiaoyan Chen, Xi Chen, Ian Horrocks +2
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or seman…
Measurable Counterfactual Local Explanations for Any Classifier
Adam White, Artur d'Avila Garcez
We propose a novel method for explaining the predictions of any classifier. In our approach, local explanations are expected to explain both the outcome of a prediction and how tha…
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
Artur d'Avila Garcez, Marco Gori, Luis C. Lamb +3
Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities,…
Unsupervised Neural-Symbolic Integration
Son N. Tran
Symbolic has been long considered as a language of human intelligence while neural networks have advantages of robust computation and dealing with noisy data. The integration of ne…
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
Tarek R. Besold, Artur d'Avila Garcez, Keith Stenning +2
This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for p…
Modelling and Analysing Cargo Screening Processes: A Project Outline
Peer-Olaf Siebers, Uwe Aickelin, David Menachof +2
The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured. Some manufacturer benchmarks exist…
Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models
Robert G. Cowell
It is often stated in papers tackling the task of inferring Bayesian network structures from data that there are these two distinct approaches: (i) Apply conditional independence t…
Propagation using Chain Event Graphs
Peter Thwaites, Jim Q. Smith, Robert G. Cowell
A Chain Event Graph (CEG) is a graphial model which designed to embody conditional independencies in problems whose state spaces are highly asymmetric and do not admit a natural pr…