Probabilistic memory-one strategies to dominate the iterated prisoner’s dilemma over networks

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGThe Iterated Prisoner’s Dilemma (IPD) has been a classical game theoretical scenario used to model behaviour interactions among agents. From the famous Axelrod’s tournament, and the successful results obtained by the Tit for Tat strategy, to the introduction of the zerodeterminant strategies in the last decade, the game theory community has been exploring the performance of multiple strategies for years. This article grounds on such previous work, studying probabilistic memory-one strategies (PMO) and using evolutionary game theory, to analyse the criteria to find the most successful set of strategies in networked topologies. The results are nearly deterministic in discrete PMO scenarios. However, results become much more complex when moving to continuous ones, and there is no optimal strategy for a given scenario. Finally, this article describes how, using machine learning and evolutionary techniques; a cluster of agents, playing synchronously and adaptively, is able to dominate the rest of the populatio

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Personalised online sales using web usage data mining

Practically every major company with a retail operation has its own web site and online sales facilities. This paper describes a toolset that exploits web usage data mining techniques to identify customer Internet browsing patterns. These patterns are then used to underpin a personalised product recommendation system for online sales. Within the architecture, a Kohonen neural network or self-organizing map (SOM) has been trained for use both offline, to discover user group profiles, and in real-time to examine active user click stream data, make a match to a specific user group, and recommend a unique set of product browsing options appropriate to an individual user. Our work demonstrates that this approach can overcome the scalability problem that is common among these types of system. Our results also show that a personalised recommender system powered by the SOM predictive model is able to produce consistent recommendations

Predicting Landslides in Costa Rica Using Self-Organizing Map Machine Learning

Landslides are natural hazards commonly understated in both number of occurrences and cost of economic impacts. The Costa Rican terrain is predominately geologically young and therefore, severely impacted by landslides. It has limited resources and infrastructure and with a large portion of the population being poor, this causes communities to build in hazardous locations and infrastructure that can be easily crippled by landslides. Being able to identify where and when landslides are going to occur is key to mitigating the effects, either by stabilizing the slope or by evacuating communities. Machine learning is one method that has been increasingly used to monitor and predict landslides in recent times. These methods do not have the shortcomings of traditional analytical methods and can be easily adapted for different locations, changing or missing data, and number of factors studied. This research proposes that Self Organizing Maps (SOM) can be used as a versatile and effective method for landslide prediction. The results of this study have shown how SOM can be used for multi scale susceptibility analysis and for prediction with use of precipitation data, by producing significant results identifying high risk areas with a varying number and combination of variables. It has also shown that when precipitation data is used, it can identify high risk locations based on precipitation amounts and static variables (slope, TWI, curvature, NDVI, etc.). At the five-time scales tested, four of the tests produced correlations between increased precipitation and higher landslides risk (6 hour r2 = 0.38, 12 hour r2 = 0.36, 1 day r2 = 0.24, 1 month r2 = 0.33). This study has shown the versatility and effectiveness of SOM by producing significant results, as well as being able to use current weather conditions to produce landslide prediction analysis

Clustering Methods for Network Data Analysis in Programming

In the modern world, data volumes are constantly increasing, and clustering has become an essential tool for identifying patterns and regularities in large datasets. The relevance of this study is associated with the growing need for effective data analysis methods in programming. The objective is to evaluate different clustering techniques within the programming domain and explore their suitability for analysing a wide range of datasets. Inductive and deductive methodologies, concrete illustrations, and visual techniques were employed. The clustering techniques were implemented using RStudio and Matlab tools. The study's findings facilitated the identification of crucial attributes of clustering techniques, including hierarchical structure, cluster quantity, and similarity metrics. The application of several data analysis and visualisation approaches, including k-means, c-means, hierarchical, least spanning tree, and linked component extraction, was illustrated. This study elucidated the clustering approaches that may be optimally employed in various contexts, resulting in enhanced precision in analyses and data-informed decision-making. The study's practical significance is in enhancing programmers' methodological toolset with tools for data analysis and processing

Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

<p>Abstract</p> <p>Background</p> <p>Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering.</p> <p>Results</p> <p>The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions.</p> <p>Conclusions</p> <p>The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.</p

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

Proceedings of 2009 Kentucky Water Resources Annual Symposium

This conference was planned and conducted as part of the state water resources research annual program with the support and collaboration of the Department of the Interior, U.S. Geological Survey and the University of Kentucky Research Foundation, under Grant Agreement Number 06HQGR0087. The views and conclusions contained in this document and presented at the symposium are those of the abstract authors and presenters and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government or other symposium organizers and sponsors