An Incremental Theorem Prover. - Murray Shanahan (IJCAI, 1987)

Search, inference and dependencies in artificial intelligence. - Murray Shanahan; Richard Southwick (Ellis Horwood, 1989)

Prediction is Deduction but Explanation is Abduction. - Murray Shanahan (IJCAI, 1989)

Representing Continuous Change in the Event Calculus. - Murray Shanahan (ECAI, 1990)

Explanation in the Situation Calculus. - Murray Shanahan (IJCAI, 1993)

Narratives in the Situation Calculus. - Rob Miller 002; Murray Shanahan (J. Log. Comput., 1994)

Default Reasoning about Spatial Occupancy. - Murray Shanahan (Artif. Intell., 1995)

A Circumscriptive Calculus of Events. - Murray Shanahan (Artif. Intell., 1995)

Noise and the Common Sense Informatic Situation for a Mobile Robot. - Murray Shanahan (AAAI/IAAI, Vol. 2, 1996)

Robotics and the Common Sense Informatic Situation. - Murray Shanahan (ECAI, 1996)

Reasoning about Discontinuities in the Event Calculus. - Rob Miller 002; Murray Shanahan (KR, 1996)

Solving the frame problem - a mathematical investigation of the common sense law of inertia. - Murray Shanahan (MIT Press, 1997)

Noise, Non-Determinism and Spatial Uncertainty. - Murray Shanahan (AAAI/IAAI, 1997)

Event Calculus Planning Revisited. - Murray Shanahan (ECP, 1997)

A Logical Account of the Common Sense Informatic Situation for a Mobile Robot. - Murray Shanahan (Electron. Trans. Artif. Intell., 1998)

The Event Calculus Explained. - Murray Shanahan (Artificial Intelligence Today, 1999)

Robot Navigation and Map Building with the Event Calculus (abstract). - Murray Shanahan; Mark Witkowski (ESCQARU, 1999)

The Ramification Problem in the Event Calculus. - Murray Shanahan (IJCAI, 1999)

The Event Calculus in Classical Logic - Alternative Axiomatisations. - Rob Miller 002; Murray Shanahan (Electron. Trans. Artif. Intell., 1999)

High-Level Robot Control through Logic. - Murray Shanahan; Mark Witkowski (ATAL, 2000)

Review: M. Shanahan, Solving the Frame Problem. - Murray Shanahan (Artif. Intell., 2000)

An abductive event calculus planner. - Murray Shanahan (J. Log. Program., 2000)

From Images to Bodies: Modelling and Exploiting Spatial Occlusion and Motion Parallax. - David A. Randell; Mark Witkowski; Murray Shanahan (IJCAI, 2001)

Some Alternative Formulations of the Event Calculus. - Rob Miller 002; Murray Shanahan (Computational Logic: Logic Programming and Beyond, 2002)

Hypothesising Object Relations from Image Transitions. - Paulo E. Santos; Murray Shanahan (ECAI, 2002)

A Logical Account of Perception Incorporating Feedback and Expectation. - Murray Shanahan (KR, 2002)

A Logic-based Algorithm for Image Sequence Interpretation and Anchoring. - Paulo E. Santos; Murray Shanahan (IJCAI, 2003)

Indirect and Conditional Sensing in the Event Calculus. - Jeremy Forth; Murray Shanahan (ECAI, 2004)

A Logic-Based Formulation of Active Visual Perception. - Murray Shanahan; David A. Randell (KR, 2004)

An attempt to formalise a non-trivial benchmark problem in common sense reasoning. - Murray Shanahan (Artif. Intell., 2004)

Event Calculus Planning Through Satisfiability. - Murray Shanahan; Mark Witkowski (J. Log. Comput., 2004)

Perception as Abduction: Turning Sensor Data Into Meaningful Representation. - Murray Shanahan (Cogn. Sci., 2005)

On Packing 2D Irregular Shapes. - Alexandros Bouganis; Murray Shanahan (ECAI, 2006)

Leaf Classification Using Navigation-Based Skeletons. - Georgios Sakellariou; Murray Shanahan (ECAI, 2006)

A Simulated Global Neuronal Workspace with Stochastic Wiring. - Dustin Connor; Murray Shanahan (AAAI Fall Symposium: AI and Consciousness, 2007)

A fast evaluation criterion for the recognition of occluded shapes. - Secil Ozen; Alexandros Bouganis; Murray Shanahan (Robotics Auton. Syst., 2007)

A Vision-Based Intelligent System for Packing 2-D Irregular Shapes. - Alexandros Bouganis; Murray Shanahan (IEEE Trans Autom. Sci. Eng., 2007)

Modeling the Neural Basis of Cognitive Integration and Consciousness. - Murray Shanahan; Dustin Connor (ALIFE, 2008)

Flexible object recognition in cluttered scenes using relative point distribution models. - Alexandros Bouganis; Murray Shanahan (ICPR, 2008)

Object Detection for a Humanoid Robot Using a Probabilistic Global Workspace. - Andreas Fidjeland; Murray Shanahan; Alexandros Bouganis (ICVW, 2008)

NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs. - Andreas Fidjeland; Etienne B. Roesch; Murray Shanahan; Wayne Luk (ASAP, 2009)

Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity. - Alexandros Bouganis; Murray Shanahan (IJCNN, 2010)

Accelerated simulation of spiking neural networks using GPUs. - Andreas Fidjeland; Murray Shanahan (IJCNN, 2010)

A computational model of a global neuronal workspace with stochastic connections. - Dustin Connor; Murray Shanahan (Neural Networks, 2010)

Serial from Parallel, Unity from Multiplicity: What Emerges from Global Workspace Architecture. - Murray Shanahan (BICA, 2011)

Establishing communication between neuronal populations through competitive entrainment. - Mark Wildie; Murray Shanahan (Frontiers Comput. Neurosci., 2011)

How well do oscillator models capture the behaviour of biological neurons? - David Bhowmik; Murray Shanahan (IJCNN, 2012)

Hierarchical clustering identifies hub nodes in a model of resting-state brain activity. - Mark Wildie; Murray Shanahan (IJCNN, 2012)

STDP produces robust oscillatory architectures that exhibit precise collective synchronization. - David Bhowmik; Murray Shanahan (IJCNN, 2013)

A Cognitive Neural Architecture as a Robot Controller. - Zafeirios Fountas; Murray Shanahan (Living Machines, 2013)

Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis. - Murray Shanahan; Verner P. Bingman; Toru Shimizu; Martin Wild; Onur Güntürkün (Frontiers Comput. Neurosci., 2013)

Three Tools for the Real-Time Simulation of Embodied Spiking Neural Networks Using GPUs. - Andreas Fidjeland; David Gamez; Murray Shanahan; Edgars Lazdins (Neuroinformatics, 2013)

Phase offset between slow oscillatory cortical inputs influences competition in a model of the basal ganglia. - Zafeirios Fountas; Murray Shanahan (IJCNN, 2014)

Does plasticity promote criticality? - Filipe Peliz Pinto Teixeira; Murray Shanahan (IJCNN, 2014)

GPU-based fast parameter optimization for phenomenological spiking neural models. - Zafeirios Fountas; Murray Shanahan (IJCNN, 2015)

Local and global criticality within oscillating networks of spiking neurons. - Filipe Peliz Pinto Teixeira; Murray Shanahan (IJCNN, 2015)

Ascribing Consciousness to Artificial Intelligence. - Murray Shanahan (CoRR, 2015)

Effects of lesions on synchrony and metastability in cortical networks. - Frantisek Vása; Murray Shanahan; Peter J. Hellyer; Gregory P. T. Scott; Joana Cabral; Robert Leech (NeuroImage, 2015)

A reservoir computing model of episodic memory. - David Bhowmik; Kyriacos Nikiforou; Murray Shanahan; Michail Maniadakis; Panos E. Trahanias (IJCNN, 2016)

Classifying Options for Deep Reinforcement Learning. - Kai Arulkumaran; Nat Dilokthanakul; Murray Shanahan; Anil Anthony Bharath (CoRR, 2016)

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders. - Nat Dilokthanakul; Pedro A. M. Mediano; Marta Garnelo; Matthew C. H. Lee; Hugh Salimbeni; Kai Arulkumaran; Murray Shanahan (CoRR, 2016)

Towards Deep Symbolic Reinforcement Learning. - Marta Garnelo; Kai Arulkumaran; Murray Shanahan (CoRR, 2016)

An Investigation of the Dynamical Transitions in Harmonically Driven Random Networks of Firing-Rate Neurons. - Kyriacos Nikiforou; Pedro A. M. Mediano; Murray Shanahan (Cogn. Comput., 2017)

The Partial Information Decomposition of Generative Neural Network Models. - Tycho M. S. Tax; Pedro A. M. Mediano; Murray Shanahan (Entropy, 2017)

SCAN: Learning Hierarchical Compositional Visual Concepts. - Irina Higgins; Nicolas Sonnerat; Loic Matthey; Arka Pal; Christopher P. Burgess; Matko Bosnjak; Murray Shanahan; Matthew M. Botvinick; Demis Hassabis; Alexander Lerchner (ICLR (Poster), 2018)

Conditional Neural Processes. - Marta Garnelo; Dan Rosenbaum; Christopher Maddison; Tiago Ramalho; David Saxton; Murray Shanahan; Yee Whye Teh; Danilo Jimenez Rezende; S. M. Ali Eslami (ICML, 2018)

Continual Reinforcement Learning with Complex Synapses. - Christos Kaplanis; Murray Shanahan; Claudia Clopath (ICML, 2018)

Deep Reinforcement Learning with Risk-Seeking Exploration. - Nat Dilokthanakul; Murray Shanahan (SAB, 2018)

Relational Deep Reinforcement Learning. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (CoRR, 2018)

Consistent Generative Query Networks. - Ananya Kumar; S. M. Ali Eslami; Danilo J. Rezende; Marta Garnelo; Fabio Viola; Edward Lockhart; Murray Shanahan (CoRR, 2018)

Deep reinforcement learning with relational inductive biases. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (ICLR (Poster), 2019)

Policy Consolidation for Continual Reinforcement Learning. - Christos Kaplanis; Murray Shanahan; Claudia Clopath (ICML, 2019)

The Animal-AI Testbed and Competition. - Matthew Crosby; Benjamin Beyret; Murray Shanahan; José Hernández-Orallo; Lucy Cheke; Marta Halina (NeurIPS (Competition and Demos), 2019)

The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition. - Benjamin Beyret; José Hernández-Orallo; Lucy Cheke; Marta Halina; Murray Shanahan; Matthew Crosby (CoRR, 2019)

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning. - Nat Dilokthanakul; Christos Kaplanis; Nick Pawlowski; Murray Shanahan (IEEE Trans. Neural Networks Learn. Syst., 2019)

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. - Sarthak Mittal; Alex Lamb; Anirudh Goyal; Vikram Voleti; Murray Shanahan; Guillaume Lajoie; Michael Mozer; Yoshua Bengio (ICML, 2020)

An Explicitly Relational Neural Network Architecture. - Murray Shanahan; Kyriacos Nikiforou; Antonia Creswell; Christos Kaplanis; David G. T. Barrett; Marta Garnelo (ICML, 2020)

Continual Reinforcement Learning with Multi-Timescale Replay. - Christos Kaplanis; Claudia Clopath; Murray Shanahan (CoRR, 2020)

Learning Diverse Representations for Fast Adaptation to Distribution Shift. - Daniel Pace; Alessandra Russo; Murray Shanahan (CoRR, 2020)

Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning. - Borja G. León; Murray Shanahan; Francesco Belardinelli (CoRR, 2020)

AlignNet: Unsupervised Entity Alignment. - Antonia Creswell; Kyriacos Nikiforou; Oriol Vinyals; Andre Saraiva 001; Rishabh Kabra; Loic Matthey; Chris Burgess 001; Malcolm Reynolds; Richard Tanburn; Marta Garnelo; Murray Shanahan (CoRR, 2020)

Unsupervised Object-Based Transition Models For 3D Partially Observable Environments. - Antonia Creswell; Rishabh Kabra; Christopher P. Burgess; Murray Shanahan (NeurIPS, 2021)

Encoders and Ensembles for Task-Free Continual Learning. - Murray Shanahan; Christos Kaplanis; Jovana Mitrovic (CoRR, 2021)

In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications. - Borja G. León; Murray Shanahan; Francesco Belardinelli (ICLR, 2022)

Abstraction for Deep Reinforcement Learning. - Murray Shanahan; Melanie Mitchell (IJCAI, 2022)

Sparse Relational Reasoning with Object-Centric Representations. - Alex F. Spies; Alessandra Russo; Murray Shanahan (CoRR, 2022)

Faithful Reasoning Using Large Language Models. - Antonia Creswell; Murray Shanahan (CoRR, 2022)

A Predictive Processing Model of Episodic Memory and Time Perception. - Zafeirios Fountas; Anastasia Sylaidi; Kyriacos Nikiforou; Anil K. Seth; Murray Shanahan; Warrick Roseboom (Neural Comput., 2022)

Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. - Antonia Creswell; Murray Shanahan; Irina Higgins (ICLR, 2023)

Schema-learning and rebinding as mechanisms of in-context learning and emergence. - Sivaramakrishnan Swaminathan; Antoine Dedieu; Rajkumar Vasudeva Raju; Murray Shanahan; Miguel Lázaro-Gredilla; Dileep George (NeurIPS, 2023)

Evaluating Large Language Model Creativity from a Literary Perspective. - Murray Shanahan; Catherine Clarke (CoRR, 2023)

Improving Activation Steering in Language Models with Mean-Centring. - Ole Jorgensen; Dylan Cope; Nandi Schoots; Murray Shanahan (CoRR, 2023)

Role play with large language models. - Murray Shanahan; Kyle McDonell; Laria Reynolds (Nat., 2023)

Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning. - Wilka Carvalho; Andrew Kyle Lampinen; Kyriacos Nikiforou; Felix Hill; Murray Shanahan (Trans. Mach. Learn. Res., 2023)

The Propensity for Density in Feed-Forward Models. - Nandi Schoots; Alex Jackson; Ali Kholmovia; Peter McBurney; Murray Shanahan (ECAI, 2024)

Simulacra as Conscious Exotica. - Murray Shanahan (CoRR, 2024)

A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI. - Seliem El-Sayed; Canfer Akbulut; Amanda McCroskery; Geoff Keeling; Zachary Kenton; Zaria Jalan; Nahema Marchal; Arianna Manzini; Toby Shevlane; Shannon Vallor; Daniel Susser; Matija Franklin; et al. (CoRR, 2024)

The Ethics of Advanced AI Assistants. - Iason Gabriel; Arianna Manzini; Geoff Keeling; Lisa Anne Hendricks; Verena Rieser; Hasan Iqbal; Nenad Tomasev; Ira Ktena; Zachary Kenton; Mikel Rodriguez; Seliem El-Sayed; Sasha Brown; et al. (CoRR, 2024)

The Propensity for Density in Feed-forward Models. - Nandi Schoots; Alex Jackson; Ali Kholmovaia; Peter McBurney; Murray Shanahan (CoRR, 2024)

Existential Conversations with Large Language Models: Content, Community, and Culture. - Murray Shanahan; Beth Singler (CoRR, 2024)

The broader spectrum of in-context learning. - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Aaditya K. Singh; Murray Shanahan (CoRR, 2024)

Still "Talking About Large Language Models": Some Clarifications. - Murray Shanahan (CoRR, 2024)

Transformers Use Causal World Models in Maze-Solving Tasks. - Alex F. Spies; William Edwards; Michael I. Ivanitskiy; Adrians Skapars; Tilman Räuker; Katsumi Inoue; Alessandra Russo; Murray Shanahan (CoRR, 2024)

Talking about Large Language Models. - Murray Shanahan (Commun. ACM, 2024)

Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models. - Lujain Ibrahim; Canfer Akbulut; Rasmi Elasmar; Charvi Rastogi; Minsuk Kahng; Meredith Ringel Morris; Kevin R. McKee; Verena Rieser; Murray Shanahan; Laura Weidinger (CoRR, 2025)

Palatable Conceptions of Disembodied Being: Terra Incognita in the Space of Possible Minds. - Murray Shanahan (CoRR, 2025)

On the generalization of language models from in-context learning and finetuning: a controlled study. - Andrew K. Lampinen; Arslan Chaudhry; Stephanie C. Y. Chan; Cody Wild; Diane Wan; Alex Ku; Jörg Bornschein; Razvan Pascanu; Murray Shanahan; James L. McClelland (CoRR, 2025)

Does It Make Sense to Speak of Introspection in Large Language Models? - Iulia M. Comsa; Murray Shanahan (CoRR, 2025)

Technological folie à deux: Feedback Loops Between AI Chatbots and Mental Illness. - Sebastian Dohnány; Zeb Kurth-Nelson; Eleanor Spens; Lennart Luettgau; Alastair Reid 002; Iason Gabriel; Christopher Summerfield; Murray Shanahan; Matthew M. Nour (CoRR, 2025)

The Xeno Sutra: Can Meaning and Value be Ascribed to an AI-Generated "Sacred" Text? - Murray Shanahan; Tara Das; Robert E. Thurman (CoRR, 2025)