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Professor Simon Lucas

Simon

Professor of Artificial Intelligence

Email: simon.lucas@qmul.ac.uk
Room Number: Peter Landin, CS 320

Research

Research Interests:

Find me on:

The main focus of my research is AI and Games ? both using games an ideal test-bed for AI research, and in using AI to make better game-playing agents, generating new games and optimising game content and game parameters.  This leads on to studying various types of machine learning, with particular emphasis on reinforcement learning and evolutionary algorithms.

I am the director of the Game AI Research Group at Queen Mary University of London  and am also the Queen Mary lead for the EPSRC funded Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI).

Research includes novel approaches to General Video Game AI.

Games are a natural source of noisy optimisation algorithms, and in our group we have developed an efficient approach to this that combines evolutionary algorithms with combinatorial bandit algorithms.  It is called the N-Tuple Bandit Evolutionary Algorithm  and will soon be made open-source: more details available on request.

Publications

    • Goodman J, Perez D, Lucas SM (2022), Visualising Multiplayer Game Spaces $nameOfConference


    • Bojarczuk K, Dvortsova I, George J et al. (2021), Measurement challenges for cyber cyber digital twins: Experiences from the deployment of facebooksww simulation system $nameOfConference


    • Ahlgren J, Bojarczuk K, Drossopoulou S et al. (2021), Facebook's cyber-cyber and cyber-physical digital twins $nameOfConference


    • Gaina RD, Devlin S, Lucas SM et al. (2021), Rolling Horizon Evolutionary Algorithms for General Video Game Playing $nameOfConference


    • Goodman J, Perez-Liebana D, Lucas S (2021), Fingerprinting Tabletop Games $nameOfConference


    • Goodman J, Lucas S (2021), Weighting NTBEA for game AI optimisation $nameOfConference

    • Ovalle A, Lucas SM (2020), Bootstrapped model learning and error correction for planning with uncertainty in model-based RL $nameOfConference


    • Lucas SM (2020), Cross-Platform Games in Kotlin $nameOfConference


    • Balla M, Lucas SM, Perez-Liebana D (2020), Evaluating Generalisation in General Video Game Playing $nameOfConference


    • Dockhorn A, Lucas S (2020), Local Forward Model Learning for GVGAI Games $nameOfConference


    • Bamford C, Lucas SM (2020), Neural Game Engine: Accurate learning of generalizable forward models from pixels. $nameOfConference


    • Volkovas R, Fairbank M, Woodward JR et al. (2020), Practical Game Design Tool: State Explorer $nameOfConference


    • Bravi I, Lucas SM (2020), Rinascimento: Using event-value functions for playing Splendor $nameOfConference


    • Gaina RD, Perez-Liebana D, Lucas SM et al. (2020), Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing $nameOfConference


    • Goodman J, Lucas S (2020), Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game. 2020 IEEE World Congress on Computational Intelligence


    • Schrum J, Gutierrez J, Volz V et al. (2020), Interactive evolution and exploration within latent level-design space of generative adversarial networks $nameOfConference


    • Montoliu R, Gaina RD, Pérez-Liebana D et al. (2020), Efficient Heuristic Policy Optimisation for a Challenging Strategic Card Game International Conference on the Applications of Evolutionary Computation


    • Tang Z, Zhu Y, Zhao D et al. (2020), Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning $nameOfConference


    • Ovalle A, Lucas SM (2020), Modulation of viability signals for self-regulatory control $nameOfConference


    • Agapitos A, Loughran R, Nicolau M et al. (2019), A survey of statistical machine learning elements in genetic programming $nameOfConference


    • Volkovas R, Fairbank M, Woodward JR et al. (2019), Extracting learning curves from puzzle games $nameOfConference


    • Lucas S, Dockhorn A, Volz V et al. (2019), A Local Approach to Forward Model Learning: Results on the Game of Life Game IEEE Conference on Games (COG)


    • Dockhorn A, Lucas S, Volz V et al. (2019), Learning Local Forward Models on Unforgiving Games IEEE Conference on Games (COG)


    • GAINA RALUCA, Lucas S, Perez-Liebana D (2019), Project Thyia: A Forever Gameplayer IEEE Conference on Games (COG)


    • Volkovas R, Fairbank M, Woodward JR et al. (2019), Mek: Mechanics prototyping tool for 2d tile-based turn-based deterministic games $nameOfConference


    • Bravi I, Perez-Liebana D, Lucas SM et al. (2019), Rinascimento: Optimising statistical forward planning agents for playing splendor $nameOfConference


    • Shoop K, Lowthorpe C, Anderson L et al. (2019), The games fusion project: Competencies for game design $nameOfConference


    • Zhao D, Lucas S, Togelius J (2019), Deep Reinforcement Learning and Games [Guest Editorial] $nameOfConference


    • Bravi I, Volz V, Lucas S (2019), Game AI hyperparameter tuning in rinascimento $nameOfConference


    • Lucas SM, Volz V (2019), Tile pattern KL-divergence for analysing and evolving game levels $nameOfConference


    • Perez D, Liu J, Abdel Samea Khalifa A et al. (2019), General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms $nameOfConference


    • Lucas S, Liu J, Bravi I et al. (2019), Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best Game Simulations Workshop (AAAI)

    • Perez-Liebana D, Gaina RD, Drageset O et al. (2019), Analysis of statistical forward planning methods in pommerman $nameOfConference

    • Gaina RD, Lucas SM, Pérez-Liébana D (2019), Tackling sparse rewards in real-time games with statistical forward planning methods $nameOfConference


    • Lucas SM (2018), Game AI Research with Fast Planet Wars Variants $nameOfConference


    • Gaina RD, Lucas SM, Perez-Liebana D (2018), General Win Prediction from Agent Experience $nameOfConference


    • Bravi I, Perez-Liebana D, Lucas SM et al. (2018), Shallow Decision-Making Analysis in General Video Game Playing $nameOfConference


    • Lucas SM, Liu J, Perez-Liebana D (2018), The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation $nameOfConference


    • Rohlfshagen P, Liu J, Perez-Liebana D et al. (2018), Pac-Man conquers academia: Two decades of research using a classic arcade game $nameOfConference


    • Guerrero-Romero C, LUCAS S, PEREZ-LIEBANA D (2018), Using a Team of General AI Algorithms to Assist Game Design and Testing 2018 IEEE Conference on Computational Intelligence and Games (CIG)


    • Volz V, Lucas SM, Schrum J et al. (2018), Evolving Mario levels in the latent space of a deep convolutional generative adversarial network $nameOfConference


    • Lucas SM, Liu J, Perez-Liebana D (2018), Efficient noisy optimisation with the multi-sample and sliding window compact genetic algorithms $nameOfConference


    • Kunanusont K, Lucas SM, Pérez-Lébana D (2018), Modeling player experience with the n-tuple bandit evolutionary algorithm $nameOfConference

    • Sironi CF, Liu J, Perez-Liebana D et al. (2018), Self-adaptive MCTS for General Video Game Playing $nameOfConference


    • Gaina RD, Lucas SM, Pérez-Liébana D (2018), Vertigø: Visualisation of rolling horizon evolutionary algorithms in GVGAI $nameOfConference

    • Gaina RD, Couetoux A, Soemers D et al. (2017), The 2016 Two-Player GVGAI Competition $nameOfConference


    • Perez-Liebana D, Stephenson M, Gaina RD et al. (2017), Introducing real world physics and macro-actions to general video game ai $nameOfConference


    • Gaina RD, Lucas SM, Perez-Liebana D (2017), Rolling horizon evolution enhancements in general video game playing $nameOfConference


    • Liu J, Perez-Liebana D, Lucas SM (2017), Bandit-based Random Mutation Hill-Climbing $nameOfConference


    • Walton-Rivers J, Williams PR, Bartle R et al. (2017), Evaluating and modelling Hanabi-playing agents $nameOfConference


    • Liu J, Togelius J, Perez-Liebana D et al. (2017), Evolving Game Skill-Depth using General Video Game AI agents $nameOfConference


    • Kunanusont K, Lucas SM, Perez-Liebana D (2017), General Video Game AI: Learning from screen capture $nameOfConference


    • Gaina RD, Lucas SM, Perez-Liebana D (2017), Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing $nameOfConference


    • Kunanusont K, Gaina RD, Liu J et al. (2017), The N-Tuple bandit evolutionary algorithm for automatic game improvement $nameOfConference


    • Agapitos A, O’Neill M, Kattan A et al. (2017), Recursion in tree-based genetic programming $nameOfConference


    • Lucas SM (2017), IEEE CIS VP-Education Vision Statement $nameOfConference


    • Gaina RD, Pérez-Liébana D, Lucas SM (2017), General video game for 2 players: Framework and competition $nameOfConference


    • Liu J, Pérez-Liébana D, Lucas SM (2017), Rolling horizon coevolutionary planning for two-player video games $nameOfConference


    • Gaina RD, Liu J, Lucas SM et al. (2017), Analysis of vanilla rolling Horizon evolution parameters in general video game playing $nameOfConference


    • Samothrakis S, Fasli M, Perez D et al. (2016), Default policies for global optimisation of noisy functions with severe noise $nameOfConference


    • Perez-Liebana D, Mostaghim S, Lucas SM (2016), Multi-objective tree search approaches for general video game playing $nameOfConference


    • Perez-Liebana D, Samothrakis S, Togelius J et al. (2016), The 2014 General Video Game Playing Competition $nameOfConference


    • Khalifa A, Perez-Liebana D, Lucas SM et al. (2016), General video game level generation $nameOfConference


    • Perez-Liebana D, Samothrakis S, Togelius J et al. (2016), Analyzing the robustness of general video game playing agents $nameOfConference


    • Williams PR, Perez-Liebana D, Lucas SM (2016), Ms. Pac-Man Versus Ghost Team CIG 2016 competition $nameOfConference


    • Samothrakis S, Perez D, Lucas SM et al. (2016), Predicting Dominance Rankings for Score-Based Games $nameOfConference


    • Perez-Liebana D, Samothrakis S, Togelius J et al. (2016), General video game AI: Competition, challenges, and opportunities $nameOfConference

    • Perez D, Mostaghim S, Samothrakis S et al. (2015), Multiobjective Monte Carlo Tree Search for Real-Time Games $nameOfConference


    • Williams PR, Walton-Rivers J, Perez-Liebana D et al. (2015), Monte Carlo Tree Search applied to co-operative problems $nameOfConference


    • Samothrakis S, Perez-Liebana D, Lucas SM et al. (2015), Neuroevolution for General Video Game Playing $nameOfConference


    • Runarsson TP, Lucas SM (2015), On imitating Connect-4 game trajectories using an approximate n-Tuple evaluation function $nameOfConference


    • Perez D, Dieskau J, Hünermund M et al. (2015), Open loop search for general video game playing $nameOfConference


    • Perez D, Samothrakis S, Lucas S (2014), Knowledge-based fast evolutionary MCTS for general video game playing $nameOfConference


    • Samothrakis S, Roberts SA, Perez D et al. (2014), Rolling horizon methods for games with continuous states and actions $nameOfConference


    • Perez D, Togelius J, Samothrakis S et al. (2014), Automated map generation for the physical traveling salesman problem $nameOfConference


    • Perez D, Powley E, Whitehouse D et al. (2014), The 2013 Multi-objective Physical Travelling Salesman Problem Competition $nameOfConference


    • Runarsson TP, Lucas SM (2014), Preference learning for move prediction and evaluation function approximation in Othello $nameOfConference


    • Lucas SM, Samothrakis S, Pérez D (2014), Fast evolutionary adaptation for Monte Carlo Tree Search $nameOfConference


    • Perez D, Powley EJ, Whitehouse D et al. (2014), Solving the physical traveling salesman problem: Tree search and macro actions $nameOfConference


    • Roberts SA, Lucas SM (2013), Measuring interestingness of continuous game problems $nameOfConference


    • Perez D, Samothrakis S, Lucas S (2013), Online and offline learning in multi-objective Monte Carlo Tree Search $nameOfConference


    • Alhejali AM, Lucas SM (2013), Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent $nameOfConference


    • Perez D, Samothrakis S, Lucas SM et al. (2013), Rolling horizon evolution versus tree search for navigation in single-player real-time games $nameOfConference


    • Lucas SM, Mateas M, Preuss M et al. (2013), Dagstuhl Manifesto $nameOfConference


    • Samothrakis S, Lucas S, Runarsson TP et al. (2013), Coevolving game-playing agents: Measuring performance and intransitivities $nameOfConference


    • Jaimez-González CR, Luna-Ramírez WA, Lucas SM (2012), A web tool for monitoring HTTP asynchronous method invocations $nameOfConference

    • Sombat W, Rohlfshagen P, Lucas SM (2012), Evaluating the enjoyability of the ghosts in Ms Pac-Man $nameOfConference


    • Roberts SA, Lucas SM (2012), Evolving spaceship designs for optimal control and the emergence of interesting behaviour $nameOfConference


    • Ashlock D, Ashlock W, Samothrakis S et al. (2012), From competition to cooperation: Co-evolution in a rewards continuum $nameOfConference


    • Runarsson TP, Lucas SM (2012), Imitating play from game trajectories: Temporal difference learning versus preference learning $nameOfConference


    • Peña L, Ossowski S, Peña JM et al. (2012), Learning and evolving combat game controllers $nameOfConference


    • Perez D, Rohlfshagen P, Lucas SM (2012), Monte Carlo Tree Search: Long-term versus short-term planning $nameOfConference


    • Perez D, Rohlfshagen P, Lucas SM (2012), The physical travelling salesman problem: WCCI 2012 competition $nameOfConference


    • Lucas SM, Rohlfshagen P, Perez D (2012), Towards more intelligent adaptive video game agents: A computational intelligence perspective $nameOfConference


    • Perez D, Rohlfshagen P, Lucas SM (2012), Monte-Carlo tree search for the physical travelling salesman problem $nameOfConference


    • Browne CB, Powley E, Whitehouse D et al. (2012), A survey of Monte Carlo tree search methods $nameOfConference


    • Jaimez-Gonzalez CR, Lucas SM, Lopez-Ornelas EJ (2011), Easy XML serialization of C# and Java objects $nameOfConference


    • Robles D, Rohlfshagen P, Lucas SM (2011), Learning non-random moves for playing Othello: Improving Monte Carlo Tree Search $nameOfConference


    • Abdullahi AA, Lucas SM (2011), Temporal difference learning with interpolated n-tuples: Initial results from a simulated car racing environment $nameOfConference


    • Alhejali AM, Lucas SM (2011), Using a training camp with genetic programming to evolve Ms Pac-Man agents $nameOfConference


    • Rohlfshagen P, Lucas SM (2011), Ms Pac-Man versus Ghost Team CEC 2011 competition $nameOfConference


    • Samothrakis S, Lucas S (2011), Approximating n-player behavioural strategy Nash equilibria using coevolution $nameOfConference


    • Samothrakis S, Robles D, Lucas S (2011), Fast approximate max-n Monte Carlo tree search for Ms Pac-Man $nameOfConference


    • Alhejali AM, Lucas SM (2010), Evolving diverse Ms. Pac-Man playing agents using genetic programming $nameOfConference


    • Samothrakis S, Lucas SM (2010), Planning using online evolutionary overfitting $nameOfConference


    • Abdullahi AA, Lucas SM (2010), Temporal difference learning with interpolated N-tuple networks: Initial results on pole balancing $nameOfConference


    • Samothrakis S, Robles D, Lucas SM (2010), A UCT agent for Tron: Initial investigations $nameOfConference


    • Lucas SM (2010), Estimating learning rates in evolution and TDL: Results on a simple grid-world problem $nameOfConference


    • Lucas SM (2010), Grammatical inference and games: Extended abstract $nameOfConference


    • Albakour MD, Kruschwitz U, Lucas S (2010), Sentence-level attachment prediction $nameOfConference


    • Ghanbari S, Woods JC, Lucas SM (2010), Object-based semi-automatic tool for content retrieval $nameOfConference


    • Robles D, Lucas SM (2009), A simple tree search method for playing Ms. Pac-Man $nameOfConference


    • Burrow P, Lucas SM (2009), Evolution versus temporal difference learning for learning to play Ms. Pac-Man $nameOfConference


    • Lucas SM (2009), Temporal difference learning with interpolated table value functions $nameOfConference


    • Romano D, Moffat D, El Rhalibi A et al. (2009), AI and Games Symposium $nameOfConference

    • Ghanbari S, Woods JC, Lucas SM (2009), BPT using multi-dimensional information for semi-automatic content retrieval $nameOfConference


    • Albakour MD, Blackwell R, Kruschwitz U et al. (2009), Managing collaboration projects using semantic email search $nameOfConference

    • Ghanbari S, Woods JC, Lucas SM (2009), Multi-dimensional BPTs for content retrieval $nameOfConference


    • Theodoridis T, Agapitos A, Hu H et al. (2009), Mechanical feature attributes for modeling and pattern classification of physical activities $nameOfConference


    • Foullon-Pérez A, Lucas SM (2009), Orientational features with the SNT-Grid $nameOfConference


    • Ghanbari S, Woods JC, Lucas SM (2009), Semi-automatic BPT for image retrieval $nameOfConference


    • Lucas SM (2009), Computational intelligence and AI in games: A New IEEE transactions $nameOfConference


    • Agapitos A, Dyson M, Lucas SM et al. (2008), Learning to recognise mental activities: Genetic programming of stateful classifiers for brain-computer interfacing $nameOfConference

    • Agapitos A, Togelius J, Lucas SM et al. (2008), Generating diverse opponents with multiobjective evolution $nameOfConference


    • Lucas SM (2008), Investigating learning rates for evolution and temporal difference learning $nameOfConference


    • Togelius J, Lucas S, Thang HD et al. (2008), The 2007 IEEE CEC simulated car racing competition $nameOfConference


    • Rudolph G, Jansen T, Lucas S et al. (2008), Parallel Problem Solving from Nature - PPSN X - 10th International Conference, Proceedings: Preface $nameOfConference

    • Ghanbari S, Woods JC, Rabiee HR et al. (2008), Wavelet domain Binary Partition Trees for image segmentation $nameOfConference


    • Theodoridis T, Agapitos A, Hu O et al. (2008), Ubiquitous robotics in physical human action recognition: A comparison between dynamic ANNs and GP $nameOfConference


    • Lucas SM (2008), Computational intelligence and games: Challenges and opportunities $nameOfConference


    • Agapitos A, Dyson M, Kovalchuk J et al. (2008), On the genetic programming of time-series predictors for supply chain management $nameOfConference


    • Rudolph G, Jansen T, Lucas S et al. (2008), Preface $nameOfConference


    • Loiacono D, Togelius J, Lanzi PL et al. (2008), The WCCI 2008 simulated car racing competition $nameOfConference


    • Lucas SM (2007), A statistically aligned recombination operator for finite state machines $nameOfConference


    • González CRJ, Lucas SM (2007), Implementing a state-based application using Web objects in XML $nameOfConference

    • Togelius J, Burrow P, Lucas SM (2007), Multi-population competitive co-evolution of car racing controllers $nameOfConference


    • Agapitos A, Togelius J, Lucas SM (2007), Multiobjective techniques for the use of state in genetic programming applied to simulated car racing $nameOfConference


    • Lo MY, Lucas SM (2007), N-gram fitness function with a constraint in a musical evolutionary system $nameOfConference


    • Ghanbari S, Woods JC, Rabiee HR et al. (2007), Wavelet domain binary partition trees for semantic object extraction $nameOfConference


    • Lucas SM, Togelius J (2007), Point-to-point car racing: An initial study of evolution versus temporal difference learning $nameOfConference


    • Marques H, Togelius J, Kogutowska M et al. (2007), Sensorless but not senseless: Prediction in evolutionary car racing $nameOfConference


    • Togelius J, De Nardi R, Lucas SM (2007), Towards automatic personalised content creation for racing games $nameOfConference


    • Agapitos A, Togelius J, Lucas SM (2007), Evolving controllers for simulated car racing using object oriented genetic programming $nameOfConference


    • Togelius J, De Nardi R, Marques H et al. (2007), Nonlinear dynamics modelling for controller evolution $nameOfConference


    • Galvan-Lopez E, Togelius J, Lucas S (2007), Towards understanding the effects of neutrality on the sudoku problem $nameOfConference


    • Togelius J, Lucas SM, De Nardi R (2007), Computational intelligence in racing games $nameOfConference


    • Lucas SM, Reynolds TJ (2007), Learning finite-state transducers: Evolution versus heuristic state merging $nameOfConference


    • Lucas SM (2007), Games technical committee $nameOfConference


    • Downton A, He J, Lucas S (2007), User-configurable OCR enhancement for online natural history archives $nameOfConference


    • Agapitos A, Lucas SM (2007), Evolving a statistics class using object oriented evolutionary programming $nameOfConference


    • Agapitos A, Lucas SM (2007), Evolving modular recursive sorting algorithms $nameOfConference


    • De Nardi R, Togelius J, Holland OE et al. (2006), Evolution of neural networks for helicopter control: Why modularity matters $nameOfConference

    • Agapitos A, Lucas SM (2006), Evolving efficient recursive sorting algorithms $nameOfConference

    • Lo MY, Lucas SM (2006), Evolving musical sequences with N-gram based trainable fitness functions $nameOfConference

    • Togelius J, Lucas SM (2006), Evolving robust and specialized car racing skills $nameOfConference

    • Moraglio A, Togelius J, Lucas S (2006), Product geometric crossover for the Sudoku puzzle $nameOfConference

    • Agapitos A, Lucas SM (2006), Learning recursive functions with object oriented genetic programming $nameOfConference


    • Lucas SM, Kendall G (2006), Evolutionary computation and games $nameOfConference


    • Togelius J, Lucas SM (2006), Arms races and car races $nameOfConference


    • Lucas SM, Runarsson TP (2006), Temporal difference learning versus co-evolution for acquiring Othello position evaluation $nameOfConference


    • Runarsson TP, Lucas SM (2005), Coevolution versus self-play temporal difference learning for acquiring position evaluation in small-board go $nameOfConference


    • Lucas SM (2005), Evolving a neural network location evaluator to play Ms. Pac-Man $nameOfConference

    • Lucas SM, Cho KT (2005), Fast convolutional OCR with the scanning N-tuple grid $nameOfConference


    • Togelius J, Lucas SM (2005), Forcing neurocontrollers to exploit sensory symmetry through hard-wired modularity in the game of Cellz $nameOfConference

    • Lucas SM (2005), ICDAR 2005 text locating competition results $nameOfConference


    • Lucas SM, Gonźalez CRJ (2005), Web-based deployment of text locating algorithms $nameOfConference

    • Togelius J, Lucas SM (2005), Evolving controllers for simulated car racing $nameOfConference

    • Lucas SM, Panaretos A, Sosa L et al. (2005), ICDAR 2003 robust reading competitions: Entries, results, and future directions $nameOfConference


    • Lucas SM, Reynolds TJ (2005), Learning deterministic finite automata with a smart state labeling evolutionary algorithm $nameOfConference


    • Lucas SM, Huang TK (2004), Sequence recognition with scanning N-tuple ensembles $nameOfConference


    • Lucas SM (2004), Cellz: A simple dynamic game for testing evolutionary algorithms $nameOfConference

    • Lucas SM (2004), Exploiting reflection in object oriented genetic programming $nameOfConference


    • Keijzer M, O’Reilly UM, Lucas S et al. (2004), Preface $nameOfConference


    • Downton AC, Lucas SM, Patoulas G et al. (2003), Computerising natural history card archives $nameOfConference


    • Lucas SM (2003), Discriminative training of the scanning N-tuple classifier $nameOfConference


    • Lucas SM (2003), Evolving finite state transducers: Some initial explorations $nameOfConference


    • Lucas SM, Patoulas G, Downton AC (2003), Fast lexicon-based word recognition in noisy index card images $nameOfConference


    • Lucas SM, Panaretos A, Sosa L et al. (2003), ICDAR 2003 robust reading competitions $nameOfConference


    • Lucas SM, Reynolds TJ (2003), Learning DFA: Evolution versus evidence driven state merging $nameOfConference


    • Ishidera E, Lucas SM, Downton AC (2002), Likelihood word image generation model for word recognition $nameOfConference

    • Lucas SM (2002), Web-based evaluation and deployment of pattern recognizers $nameOfConference

    • Lucas S (2002), Evolving spring-mass models: A test-bed for graph encoding schemes $nameOfConference


    • Ishidera E, Lucas SM, Downton AC (2002), Top-down likelihood word image generation model for holistic word recognition $nameOfConference


    • Lucas S (2001), Efficient graph-based dictionary search and its application to text-image searching $nameOfConference


    • Downton AC, Tarns AC, Wells GJ et al. (2001), Constructing Web-based legacy index card archives-architectural design issues and initial data acquisition $nameOfConference


    • Lucas SM, Tams AC, Cho SJ et al. (2001), Robust word recognition for museum archive card indexing $nameOfConference


    • Lucas S, Beattie P, Coy J (2000), Automatic evaluation of adaptive algorithms over the Internet $nameOfConference

    • Lucas S, Sarampalis K (2000), Automatic evaluation of algorithms over the Internet $nameOfConference

    • Lucas S (2000), Efficient best-first dictionary search given graph-based input $nameOfConference

    • Lucas S (2000), Specifying intrinsically adaptive architectures $nameOfConference


    • Downton AC, Lucas SM (1999), Interactive archive card index conversion and verification $nameOfConference

    • Lucas S (1999), Rapid best first retrieval from massive dictionaries with poorly segmented inputs $nameOfConference

    • Lucas S (1999), Designing video-joysticks $nameOfConference

    • Downton AC, Lucas SM (1999), Interactive archive card index conversion and verification $nameOfConference

    • Lucas S (1999), Rapid best first retrieval from massive dictionaries with poorly segmented inputs $nameOfConference

    • Downton AC, Lucas SM, Du L (1999), Lazy evaluation for best-first contextual handwriting recognition $nameOfConference


    • Downton AC, Lucas SM, Cracknell C (1998), Document and Handwriting Analysis for Archive Index Card Conversion $nameOfConference

    • Lucas SM (1998), Real-time face recognition with the continuous n-tuple classifier $nameOfConference

    • Lucas SM (1998), Spatially aware rapid retrieval system (SPARRS) $nameOfConference

    • Siddiqi AA, Lucas SM (1998), Comparison of matrix rewriting versus direct encoding for evolving neural networks $nameOfConference

    • Lucas SM (1998), Continuous n-tuple classifier and its application to real-time face recognition $nameOfConference


    • Lucas SM (1998), Fast exact parallel implementation of the k-nearest neighbour pattern classifier $nameOfConference

    • Du L, Downton AC, Lucas SM (1997), A unified object-Oriented toolkit for discrete contextual computer vision $nameOfConference

    • Lucas SM (1997), Forward-backward building blocks for evolving neural networks with intrinsic learning behaviours $nameOfConference

    • Lucas SM (1997), Continuous n-tuple classifier and its application to face recognition $nameOfConference


    • Lucas SM (1997), Forward-backward building blocks for evolving neural networks with intrinsic learning behaviours $nameOfConference


    • Lucas SM (1997), Forward-backward building blocks for evolving neural networks with intrinsic learning behaviours $nameOfConference


    • Du L, Downton AC, Lucas SM et al. (1997), Generalized contextual recognition of hand-printed documents using semantic trees with lazy evaluation $nameOfConference

    • Lucas SM (1996), Rapid best-first retrieval from massive dictionaries $nameOfConference


    • Lucas S (1996), Can scanning n-tuple classifiers be improved by pre-transforming training data? $nameOfConference

    • Du L, Lucas SM, Downton AC (1996), Computationally efficient contextual processing for handwritten forms $nameOfConference

    • Amiri A, Du L, Downton AC et al. (1996), Optimised hand-printed character recognition using neural network cascades $nameOfConference

    • Lucas S (1996), Rapid content-based retrieval from document image databases $nameOfConference

    • Lucas S, Amiri A (1996), Statistical syntactic methods for high-performance OCR $nameOfConference


    • Downton AC, Amiri A, Du L et al. (1995), Configurable toolkit approach to handwritten forms recognition $nameOfConference

    • Lucas SM (1995), Rapid best-first retrieval from massive dictionaries by lazy evaluation of a syntactic neural network $nameOfConference

    • Lucas SM (1995), Rapid searching of massive dictionaries given uncertain inputs $nameOfConference

    • Lucas S, Amiri A (1995), Recognition of chain-coded handwritten character images with scanning n-tuple method $nameOfConference


    • Lucas SM (1995), High performance OCR with syntactic neural networks $nameOfConference


    • Lucas SM (1995), Towards the open ended evolution of neural networks $nameOfConference


    • Lucas S, Amiri A, Hanlon SJ (1994), Robust syntactic recognition techniques for OCR $nameOfConference

    • Lucas S (1994), Structuring chromosomes for context-free grammar evolution $nameOfConference

    • Lucas S, Vidal E, Amiri A et al. (1994), A comparison of syntactic and statistical techniques for off-line OCR $nameOfConference


    • Lucas S, Zhao Z, Cawley G et al. (1993), On decomposing MLPS $nameOfConference


    • Lucas S, Zhao Z, Cawley G et al. (1993), On decomposing MLPs $nameOfConference

    • Lucas S, Zhao Z, Cawley G et al. (1993), Pattern recognition with the decomposed multilayer perceptron $nameOfConference


    • Lucas SM, Damper RI (1992), A connectionist approach to text-phonemics translation using syntactic neural networks $nameOfConference


    • Lucas S (1991), Algebraic learning in syntactic neural networks $nameOfConference

    • Lucas S, Damper B (1991), Syntactic neural networks for text-phonetics translation $nameOfConference


    • Lucas SM, Damper RI (1990), Signature verification with a syntactic neural net $nameOfConference


    • Lucas SM, Damper RI (1990), Syntactic Neural Networks $nameOfConference


    • Lucas SM, Damper RI (1989), New learning paradigm for neural networks $nameOfConference

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