Cover Tree Bayesian Reinforcement Learning

This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it suitable for many reinforcement learning problems in continuous state spaces. We demonstrate this in an experimental comparison with least squares policy iteration

A Bayesian Ensemble Regression Framework on the Angry Birds Game

An ensemble inference mechanism is proposed on the Angry Birds domain. It is based on an efficient tree structure for encoding and representing game screenshots, where it exploits its enhanced modeling capability. This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem. To this direction, we assume that each type of object material and bird pair has its own Bayesian linear regression model. In this way, a multi-model regression framework is designed that simultaneously calculates the conditional expectations of several objects and makes a target decision through an ensemble of regression models. Learning procedure is performed according to an online estimation strategy for the model parameters. We provide comparative experimental results on several game levels that empirically illustrate the efficiency of the proposed methodology.Comment: Angry Birds AI Symposium, ECAI 201

Data-driven estimation of flights’ hidden parameters

This paper presents a data-driven methodology for the estimation of flights’ hidden parameters, combining mechanistic and AI/ML models. In the context of this methodology the paper studies several AI/ML methods and reports on evaluation results for estimating hidden parameters, in terms of mean absolute error. In addition to the estimation of hidden parameters themselves, this paper examines how these estimations affect the prediction of KPIs regarding the efficiency of flights using a mechanistic model. Results show the accuracy of the proposed methods and the benefits of the proposed methodology. Indeed, the results show significant advances of data-driven methods to estimate hidden parameters towards predicting KPIs.This work has received funding from SESAR Joint Undertaking (JU) within SIMBAD project under grant agreement No 894241. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the UnionPeer ReviewedPostprint (author's final draft

HYBRID COMPUTATIONAL INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND CONTROL PROBLEMS

COMPUTATIONAL INTELLIGENCE IS AN AREA OF COMPUTING ALLOWING IMPRECISION, UNCERTAINTY AND PARTIAL TRUTH TO PROCESS AND THEREFORE ACHIEVES ROBUSTNESS AND LOWSOLUTION COST. AMONG THE FIELDS ACCOMMODATED TO THE WEB OF COMPUTATIONAL INTELLIGENCE, NEURAL NETWORKS, FUZZY SYSTEMS AND GENETIC ALGORITHMS ARE ON TOP OFPREFERENCES IN SCIENTIFIC RESEARCH. HYBRID COMPUTATIONAL INTELLIGENT SYSTEMSCONTAIN AN AGGREGATION OF ANY TWO OF THESE APPROACHES, WHERE EACH ONE CONTRIBUTES EFFECTIVELY TO THE DESIGN AND PERFORMANCE CAPABILITY OF THE HYBRID APPROACH. THE PRIMARY KEY OF THIS THESIS IS TO PRESENT HYBRID APPROACHES IN THE FIELDOF COMPUTATIONAL INTELLIGENCE CONCERNING NEURAL NETWORKS, FUZZY SYSTEMS AND GENETIC ALGORITHMS FOR SOLVING PROBLEMS IN THE AREAS OF PATTERN RECOGNITION ANDCONTROL. WE TRY TO EXAMINE THE EXISTING COMBINATIONS IN TERMS OF METHODOLOGIES, ARCHITECTURES AND APPLICATIONS. THE GUIDING PRONCIPLE IS TO SEE HOW EACH FIELD IS INFLUENCED BY ANY OTHER UNDER A CRITICAL VIEW AND TO DISTINGUISH AND SEPARATE EACH HYBRID APPROACH INTO GENERAL CATEGORIES CONCERNING DIAGNOSTIC AND CONTROL PROBLEMS.Η ΣΥΜΒΟΛΗ ΤΗΣ ΔΙΑΤΡΙΒΗΣ ΑΦΟΡΑ ΣΤΗΝ ΑΝΑΠΤΥΞΗ ΕΥΦΥΩΝ ΤΕΧΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΙΚΗΣ ΝΟΗΜΟΣΥΝΗΣ ΓΙΑ ΤΗΝ ΕΠΙΛΥΣΗ ΠΡΟΒΛΗΜΑΤΩΝ ΑΝΑΓΝΩΡΙΣΗΣ ΠΡΟΤΥΠΩΝ (ΤΑΞΙΝΟΜΗΣΗ ΚΑΙ ΔΙΑΓΝΩΣΗ) ΚΑΙ ΑΣΑΦΟΥΣ ΕΛΕΓΧΟΥ. ΟΙ ΤΕΧΝΙΚΕΣ ΠΟΥ ΑΝΑΠΤΥΧΘΗΚΑΝ ΚΑΙ ΕΞΕΤΑΣΤΗΚΑΝ ΧΑΡΑΚΤΗΡΙΖΟΝΤΑΙ ΣΤΗΝ ΠΛΕΙΟΨΗΦΙΑ ΤΟΥΣ ΑΠΟ ΓΕΝΙΚΟΤΗΤΑ ΚΑΙ ΜΠΟΡΟΥΝ ΝΑ ΧΡΗΣΙΜΟΠΟΙΗΘΟΥΝ ΓΙΑ ΤΗΝ ΕΠΙΛΥΣΗ ΠΡΟΒΛΗΜΑΤΩΝ ΣΕ ΕΝΑ ΕΥΡΥ ΦΑΣΜΑ ΕΦΑΡΜΟΓΩΝ. ΛΟΓΩ ΤΗΣ ΝΕΥΡΩΝΙΚΗΣ ΚΑΙ ΓΕΝΕΤΙΚΗΣ ΦΥΣΗΣ ΤΟΥΣ, ΟΙ ΑΝΑΠΤΥΧΘΕΙΣΕΣ ΜΕΘΟΔΟΙ ΧΑΡΑΚΤΗΡΙΖΟΝΤΑΙ ΑΠΟ ΥΨΗΛΟ ΒΑΘΜΟΠΑΡΑΛΛΗΛΙΑΣ ΓΕΓΟΝΟΣ ΠΟΥ ΚΑΘΙΣΤΑ ΕΛΚΥΣΤΙΚΗ ΤΗ ΧΡΗΣΙΜΟΠΟΙΗΣΗ ΤΟΥΣ ΣΕ ΠΟΛΥΠΛΟΚΑΠΡΟΒΛΗΜΑΤΑ. Η ΔΙΑΤΡΙΒΗ ΜΠΟΡΕΙ ΝΑ ΧΩΡΙΣΤΕΙ ΣΕ ΔΥΟ ΜΕΓΑΛΑ ΤΜΗΜΑΤΑ ΜΕ ΒΑΣΗ ΤΟ ΕΙΔΟΣ ΜΑΘΗΣΗΣ ΤΩΝ ΜΕΘΟΔΩΝ ΠΟΥ ΑΝΑΠΤΥΧΤΗΚΑΝ: (Α) ΥΒΡΙΔΙΚΕΣ ΤΕΧΝΙΚΕΣ ΠΟΥ ΒΑΣΙΖΟΝΤΑΙ ΣΕ ΕΝΙΣΧΥΝΤΙΚΗ ΜΑΘΗΣΗ ΓΙΑ ΤΗΝ ΕΥΡΕΣΗ ΠΑΡΑΜΕΤΡΩΝ ΠΟΥ ΑΦΟΡΟΥΝ ΤΗΝ ΣΥΝΑΡΤΗΣΗ ΣΥΜΜΕΤΟΧΗΣ ΚΑΙ ΠΕΡΑΙΤΕΡΩ ΤΗΝ ΠΡΟΣΑΡΜΟΣΤΙΚΗ ΚΑΤΑΣΚΕΥΗ ΑΣΑΦΩΝ ΕΛΕΓΚΤΩΝ, ΚΑΙ (Β) ΥΒΡΙΔΙΚΕΣ ΤΕΧΝΙΚΕΣ ΜΑΘΗΣΗΣ ΜΕ Η ΧΩΡΙΣ ΕΠΙΒΛΕΨΗ ΓΙΑ ΤΑΞΙΝΟΜΗΣΗ ΚΑΙ ΟΜΑΔΟΠΟΙΣΗ ΠΡΟΤΥΠΩΝ

Real-coded Genetic Optimization of Fuzzy Clustering

: A genetic approach is developed, which is suitable for the optimization of fuzzy c-means clustering. The approach is based on real encoding of the prototype variables (cluster centers) and uses appropriate genetic operators and techniques to optimize the clustering criterion. Experimental results concerning difficult clustering problems show that the proposed approach is very successful in generating fuzzy partitions and prototypes and outperforms the fuzzy c-means algorithm in terms of the correct placement of patterns into partitions. 1 INTRODUCTION The task of pattern classification and recognition typically constitutes a major component of an intelligent diagnostic system. Pattern classification can be viewed as including two steps: first, a phase of clustering given samples, and second, classification of new samples based on the knowledge of clusters. Pattern clustering considers a set of unlabeled data objects and seeks to find natural groupings amongst the exemplars. The clus..

A Bayesian Reinforcement Learning framework Using Relevant Vector Machines

In this work we present an advanced Bayesian formulation to the task of control learning that employs the Relevance Vector Machines (RVM) generative model for value function evaluation. The key aspect of the proposed method is the design of the discount return as a generalized linear model that constitutes a well-known probabilistic approach. This allows to augment the model with advantageous sparse priors provided by the RVM's regression framework. We have also taken into account the significant issue of selecting the proper parameters of the kernel design matrix. Experiments have shown that our method produces improved performance in both simulated and real test environments

Incremental training of Markov mixture models

Abstract. This paper presents an incremental approach for training a Markov mixture model to a set of sequences of discrete states. Starting from a single Markov model that captures the background information, at each step a new component is added to the mixture in order to improve the data fit. This is done by making at first an exploration of a relevant parametric space to initialize the new component, based on an extension of the k-means algorithm. Then, by performing a two-stage scheme of the EM algorithm, the new component is optimally incorporated to the body of the current mixture. To assess the effectiveness of the proposed method, we have conducted experiments with several data sets and we make a performance comparison with the classical mixture model.