Frederic D. Maire

Evolutionary Game Design

It is easy to create new combinatorial games but more difficult to predict those that will interest human players. We examine the concept of game quality, its automated measurement through self-play simulations, and its use in the evolutionary search for new high-quality games. A general game system called Ludi is described and experiments conducted to test its ability to synthesize and evaluate new games. Results demonstrate the validity of the approach through the automated creation of novel, interesting, and publishable games.

Intelligent Data Engineering and Automated Learning - IDEAL 2005

Data Mining and Knowledge Engineering.- EXiT-B: A New Approach for Extracting Maximal Frequent Subtrees from XML Data.- Synthetic Environment Representational Semantics Using the Web Ontology Language.- New Rules for Hybrid Spatial Reasoning.- Using Pre-aggregation for Efficient Spatial Query Processing in Sensor Environments.- Model Trees for Classification of Hybrid Data Types.- Finding Uninformative Features in Binary Data.- Knowledge Reduction of Rough Set Based on Partition.- Multiresolution Analysis of Connectivity.- Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval.- Unsupervised Image Segmentation Using Penalized Fuzzy Clustering Algorithm.- Multi-attributes Image Analysis for the Classification of Web Documents Using Unsupervised Technique.- Automatic Image Annotation Based on Topic-Based Smoothing.- A Focused Crawler with Document Segmentation.- An Intelligent Grading System Using Heterogeneous Linguistic Resources.- Probabilistic Data Generation for Deduplication and Data Linkage.- Mining Job Logs Using Incremental Attribute-Oriented Approach.- Dimensional Reduction of Large Image Datasets Using Non-linear Principal Components.- Classification by Instance-Based Learning Algorithm.- Analysis/Synthesis of Speech Signals Based on AbS/OLA Sinusoidal Modeling Using Elliptic Filter.- Robust Model Adaptation Using Mean and Variance Transformations in Linear Spectral Domain.- Using Support Vector Machine for Modeling of Pulsed GTAW Process.- Design of Simple Structure Neural Voltage Regulator for Power Systems.- EEG Source Localization for Two Dipoles in the Brain Using a Combined Method.- Intelligent Control of Micro Heat Exchanger with Locally Linear Identifier and Emotional Based Controller.- Identification of Anomalous SNMP Situations Using a Cooperative Connectionist Exploratory Projection Pursuit Model.- Learning Algorithms and Systems.- Neural Networks: A Replacement for Gaussian Processes?.- A Dynamic Merge-or-Split Learning Algorithm on Gaussian Mixture for Automated Model Selection.- Bayesian Radial Basis Function Neural Network.- An Empirical Study of Hoeffding Racing for Model Selection in k-Nearest Neighbor Classification.- Designing an Optimal Network Using the Cross-Entropy Method.- Generating Predicate Rules from Neural Networks.- Improving Ensembles with Classificational Cellular Automata.- A Gradient BYY Harmony Learning Algorithm on Mixture of Experts for Curve Detection.- A Novel Anomaly Detection Using Small Training Sets.- Induction of Linear Decision Trees with Real-Coded Genetic Algorithms and k-D Trees.- Intelligent Predictive Control of a 6-Dof Robotic Manipulator with Reliability Based Performance Improvement.- Sequential Search for Decremental Edition.- Bearing Similarity Measures for Self-organizing Feature Maps.- Efficient Spatial Clustering Algorithm Using Binary Tree.- Cluster Analysis of High-Dimensional Data: A Case Study.- Universal Clustering with Family of Power Loss Functions in Probabilistic Space.- Circular SOM for Temporal Characterisation of Modelled Gene Expressions.- Recursive Self-organizing Map as a Contractive Iterative Function System.- Differential Priors for Elastic Nets.- Graphics Hardware Implementation of the Parameter-Less Self-organising Map.- Weighted SOM-Face: Selecting Local Features for Recognition from Individual Face Image.- SOM-Based Novelty Detection Using Novel Data.- Multi-level Document Classifications with Self-organising Maps.- Bioinformatics.- Predictive Vaccinology: Optimisation of Predictions Using Support Vector Machine Classifiers.- Evolving Neural Networks for the Classification of Malignancy Associated Changes.- Matching Peptide Sequences with Mass Spectra.- Extraction by Example: Induction of Structural Rules for the Analysis of Molecular Sequence Data from Heterogeneous Sources.- A Multi-population ? 2 Test Approach to Informative Gene Selection.- Gene Selection of DNA Microarray Data Based on Regularization Networks.- Application of Mixture Models to Detect Differentially Expressed Genes.- A Comparative Study of Two Novel Predictor Set Scoring Methods.- Deriving Matrix of Peptide-MHC Interactions in Diabetic Mouse by Genetic Algorithm.- SVM Based Prediction of Bacterial Transcription Start Sites.- Exploiting Sequence Dependencies in the Prediction of Peroxisomal Proteins.- Protein Fold Recognition Using Neural Networks and Support Vector Machines.- Agents and Complex Systems.- Support Tool for Multi-agent Development.- A Hybrid Agent Architecture for Modeling Autonomous Agents in SAGE.- Toward Transitive Dependence in MAS.- An Architecture for Multi-agent Based Self-adaptive System in Mobile Environment.- Autonomous and Dependable Recovery Scheme in UPnP Network Settings.- A Transitive Dependence Based Social Reasoning Mechanism for Coalition Formation.- A Multi-agent Based Context Aware Self-healing System.- Combining Influence Maps and Cellular Automata for Reactive Game Agents.- Patterns in Complex Systems Modeling.- Global Optimization Using Evolutionary Algorithm Based on Level Set Evolution and Latin Square.- Co-evolutionary Rule-Chaining Genetic Programming.- A Dynamic Migration Model for Self-adaptive Genetic Algorithms.- Financial Engineering.- A Multicriteria Sorting Procedure for Financial Classification Problems: The Case of Business Failure Risk Assessment.- Volatility Modelling of Multivariate Financial Time Series by Using ICA-GARCH Models.- Volatility Transmission Between Stock and Bond Markets: Evidence from US and Australia.- A Machine Learning Approach to Intraday Trading on Foreign Exchange Markets.

Rule-extraction by backpropagation of polyhedra

The core problem of rule-extraction from feed-forward networks is an inversion problem. In this article, we solve this inversion problem by backpropagating unions of polyhedra. We obtain as a by-product a new rule-extraction technique for which the fidelity of the extracted rules can be made arbitrarily high.

Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks

Active research into processes and techniques for extracting the knowledge embedded within trained artificial neural networks has continued unabated for almost ten years. Given the considerable effort invested to date, what progress has been made? What lessons have been learned? What direction should the field take from here? This paper seeks to answer these questions. The focus is primarily on techniques for extracting rule-based explanations from feed-forward ANNs since, to date, the preponderance of the effort has been expended in this arena. However the paper also briefly reviews the broadening overall agenda for ANN knowledge-elicitation. Finally the paper identifies some of the key research questions including the search for criteria for deciding in which problem domains these techniques are likely to out-perform techniques such as Inductive Decision Trees.

Implementation of Kernel Methods on the GPU

Kernel methods such as kernel principal component analysis and support vector machines have become powerful tools for pattern recognition and computer vision. Unfortunately the high computational cost of kernel methods is a limiting factor for real-time classification tasks when running on the CPU of a standard PC. Over the last few years, commodity Graphics Processing Units (GPU) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single CPU. GPUs are inexpensive and are becoming ubiquitous (desktops, laptops, PDAs, cell phones). In this paper, we present a face recognition system based on kernel methods running on the GPU. This GPU implementation is twenty eight times faster than the same optimized application running on the CPU.

A partial order for the M-of-N rule-extraction algorithm

We present a method to unify the rules obtained by the M-of-N rule-extraction technique. The rules extracted from a perceptron by the M-of-N algorithm are in correspondence with sets of minimal Boolean vectors with respect to the classical partial order defined on vectors. Our method relies on a simple characterization of another partial order defined on Boolean vectors. We show that there exists also a correspondence between sets of minimal Boolean vectors with respect to this order and M-of-N rules equivalent to a perceptron. The gain is that fewer rules are generated with the second order. Independently, we prove that deciding whether a perceptron is symmetric with respect to two variables is NP-complete.

Obstacle-free range determination for rail track maintenance vehicles

Maintenance trains travel in convoy. In Australia, only the first train of the convoy pays attention to the track sig-nalization (the other convoy vehicles simply follow the preceding vehicle). Because of human errors, collisions can happen between the maintenance vehicles. Although an anti-collision system based on a laser distance meter is already in operation, the existing system has a limited range due to the curvature of the tracks. In this paper, we introduce an anti-collision system based on vision. The two main ideas are, (1) to warp the camera image into an image where the rails are parallel through a projective transform, and (2) to track the two rail curves simultaneously by evaluating small parallel segments. The performance of the system is demonstrated on an image dataset.

Hands-free mouse-pointer manipulation using motion-tracking and speech recognition

Technology is advancing at a rapid pace, automating many everyday chores in the process. Information technology (IT) is changing the way we perform work and providing society with a multitude of entertainment options. Unfortunately, in the past designers of many software systems have not considered the disabled as active users of technology, and thus this significant part of the world population has often been neglected. A change in this mindset has been emerging in recent years, however, as private-sector organizations and governments have started to realize that including this user group is not only profitable, but also beneficial to society as a whole. This paper introduces an alternative method to the traditional mouse input device, using a modified Lucas-Kanade optical-flow algorithm for tracking head movements, and speech recognition to activate mouse buttons.

Topological SLAM Using Fast Vision Techniques

In this paper we propose a method for vision only topological simultaneous localisation and mapping (SLAM). Our approach does not use motion or odometric information but a sequence of noisy visual measurements observed by traversing an environment. In particular, we address the perceptual aliasing problem which occurs using external observations only in topological navigation. We propose a Bayesian inference method to incrementally build a topological map by inferring spatial relations from the sequence of observations while simultaneously estimating the robots location. The algorithm aims to build a small map which is consistent with local adjacency information extracted from the sequence measurements. Local adjacency information is incorporated to disambiguate places which otherwise would appear to be the same. Experiments in an indoor environment show that the proposed technique is capable of dealing with perceptual aliasing using visual observations only and successfully performs topological SLAM.

Vision based anti-collision system for rail track maintenance vehicles

Maintenance trains travel in convoy. In Australia, only the first train of the convoy pays attention to the track signalization (the other convoy vehicles simply follow the preceding vehicle). Because of human errors, collisions can happen between the maintenance vehicles. Although an anti-collision system based on a laser distance meter is already in operation, the existing system has a limited range due to the curvature of the tracks. In this paper, we introduce an anti-collision system based on vision. The proposed system induces a 3D model of the track as a piecewise quadratic function (with continuity constraints on the function and its derivative). The geometric constraints of the rail tracks allow the creation of a completely self-calibrating system. Although road lane marking detection algorithms perform well most of the time for rail detection, the metallic surface of a rail does not always behave like a road lane marking. Therefore we had to develop new techniques to address the specific problems of the reflectance of rails.