WELCOME TO BRACIS BRACIS is one of the most important events in Brazil for researchers in the field of Artificial and Computational intelligence. The domain of the conference includes traditional and original topics of both areas and it aims to promote theories and novel applications dealing with the use and analysis of AI/CI techniques in various related fields. BRACIS also aims for the promotion of international level research by exchanging scientific ideas among researchers, practitioners, scientists and engineers. The yearly event series started in 2012 from the combination of the Brazilian Symposium on Artificial Intelligence – SBIA (21 editions) and the Brazilian Symposium on Neural Networks – SBRN (12 editions). TOPICS OF INTEREST: • Agent-based and multi-agent systems • Cognitive modeling and human interaction • Constraints and Search • Foundations of AI • Distributed AI • Information retrieval, integration, and extraction • Knowledge Representation and Reasoning (including Commonsense Reasoning, Model-Based Reasoning, Probabilistic Reasoning, Approximate Reasoning) • Knowledge Representation and Reasoning in Ontologies and the Semantic Web • Logic based Knowledge Representation and Reasoning • Natural Language Processing • Planning and Scheduling • Evolutionary Computation • Fuzzy Systems • Neural Networks • Machine Learning and Data Mining • Metalearning • Molecular and quantum computing • Pattern Recognition and Cluster Analysis • Swarm Intelligence • Hybrid Systems • Bioinformatics and bioengineering • Combinatorial and numerical optimization • Computer Vision • Education • Forecasting • Game Playing and Intelligent Interactive Entertainment • Intelligent Robotics • Multidisciplinary AI and CI • Other AI and CI related topics ASOC JOURNAL AND SPECIAL ISSUES Authors of selected papers will be invited to submit extended versions of their work to be appreciated for publication in a fast track process of Applied Soft Computing (ASOC) - Elsevier +ISSN: 1568-4946 (Impact Factor 2.857) Additionally, two special issues are also confirmed this year: Natural Computing – Springer +ISSN: 1567-7818 (Impact factor 0.757) New Generation Computing – Springer and Ohmsha publisher +ISSN: 0288-3635 (Impact factor 0.533) If you have more than 2 papers accepted in BRACIS, ENIAC and KDMILE, you must pay the Extra Paper Fee for each paper beyond the second.
Fusion Approaches of Feature Selection Algorithms for Classification Problems
The large amount of data produced by applications in recent years needs to be analyzed in order to extract valuable underlying information from them. Machine learning algorithms are useful tools to perform this task, but usually it is necessary to reduce complexity of data using feature selection algorithms. As usual, many algorithms were proposed to reduce dimension of data, each one with its own advantages and drawbacks. The variety of algorithms leads to either choose one algorithm or to combine several methods. The last option usually brings better performance. Based on this, this paper proposes an analysis of two distinct approaches of combining feature selection algorithms (decision and data fusion). This analysis was made in supervised classification context using real and synthetic datasets. Results showed that one proposed approach (decision fusion) has achieved the best results for the majority of datasets
An Unsupervised Particle Swarm Optimization Approach for Opinion Clustering
Supervised machine learning (ML) and lexiconbased are the most frequent approaches for opinion mining (OM), but they require considerable effort for preparing the training data and to build the opinion lexicon, respectively. This paper presents two unsupervised approaches for OM based on Particle Swarm Optimization (PSO). The PSO-based approaches were evaluated by eighteen experiments with different corpora types, domains, language, class balancing and pre-processing techniques. The proposed approaches achieved better accuracy on twelve experiments. Best results were obtained on corpora with a reduced number of dimensions and for specific domains. Best accuracy (0.79) was obtained by Discrete IDPSO on the OBCC corpus, outperforming supervised ML and lexicon-based approaches for this corpus.
Data Clustering Using Group Search Optimization with Alternative Fitness Functions
Data clustering is an important tool for statistical data analysis and exploration, and it has been successfully applied in many fields like image understanding, bioinformatics, big data mining, and so on. From the past few decades, Evolutionary Algorithms (EAs) have been introduced to deal with clustering task, given their global search capabilities and their mechanisms to escape from local minima points. EAs execution is driven in an attempt to optimize a criterion function, also known as fitness function. In this work, we evaluate the influence of the fitness function on Group Search Optimization (GSO) meta-heuristic when applied to data clustering. Three different fitness function are proposed to GSO. Experiments are performed on twelve benchmark data sets obtained from UCI Machine Learning Repository to evaluate the performance of all alternative GSO models in comparison to other well-known partitional clustering methods from literature.
Novelty Detection Based on Genuine Normal and Artificially Generated Novelty Examples
One-class classification (OCC) is an important problem with applications in several different areas such as outlier detection and machine monitoring. Since in OCC there are no examples of the novelty class, the description generated may be a tight or a bulky description. Both cases are undesirable. In order to create a proper description, the presence of examples of the novelty class is very important. However, such examples may be rare or absent during the modeling phase. In these cases, the artificial generation of novelty samples may overcome this limitation. In this work it is proposed a two steps approach for generating artificial novelty examples in order to guide the parameter optimization process. The results show that the adopted approach has shown to be competitive with the results achieved when using real (genuine) novelty samples.
Discriminating beetwen Brazilian and European Portuguese national varieties on Twitter texts
Twitter is one of the most used social media with users generating about 1 million messages per day. As a result of the expansion of this microblog, there is a diversity of languages used by users and many studies aimed at identifying the language of tweets. The third most used language on Twitter is Portuguese, a pluricentric language with two national standard varieties: Brazilian Portuguese and European Portuguese. Identifying a language variety may positively impact various Natural Language Processing tasks, but accomplishing this task is still regarded as one of the bottlenecks in this area, especially when combined with another bottleneck, language identification applied to short texts. Thus, given these challenges, this paper provides a current view on the automatic discrimination of the two main Portuguese language varieties on Twitter texts by using an acknowledged approach with different techniques and features in order to get an optimum configuration to fit our problem. Results reached 0.9271 for accuracy using an ensemble method, which combines character 6- grams and word unigrams and bigrams.
A Comparative Study of Group Profiling Techniques in Co-Authorship Networks
Group profiling methods aim to construct a descriptive profile for communities in complex networks. The application of such methods in the analysis of co-authorship networks enables us to move forward in understanding the scientific communities, leading to new approaches to strengthen and expand scientific collaboration networks. This task is similar to the document cluster labeling task, which encourages the adaptation of cluster labeling methods for group profiling problems. In this work, we present a comparative study of group profiling and cluster labeling algorithms in a co-authorship network. A qualitative survey was conducted to evaluate the generated profiles, as well as the pros and cons of different profiling strategies, were analyzed with concrete examples. The results demonstrated a similar performance of both group profiling and cluster labeling methods.
Exploring unsupervised features in Conditional Random Fields for Spanish Named Entity Recognition
Unsupervised features such as word representations mostly given by word embeddings have been shown significantly improve semi supervised Named Entity Recognition (NER) for English language. In this work we investigate whether unsupervised features can boost (semi) supervised NER in Spanish. To do so, we use word representations and collocations as additional features in a linear chain Conditional Random Field (CRF) classifier. Experimental results (82.44% F-score on the CoNLL- 2002 corpus and 65.72% F-score on Ancora Corpus) show that our approach is comparable to some state-of-art Deep Learning approaches for Spanish, in particular when using cross-lingual Word Representations.
Tabu Search with Ejection Chain for the Biobjective Adjacent-only Quadratic Spanning Tree
Given an edge-weighted simple graph G, the minimum quadratic spanning tree problem consists in finding a spanning tree of G such that the sum of the weights of its edges plus the sum of the product of the weights of pairs of edges is minimum over all spanning trees of G. When the product of the weights of pairs of edges is calculated only for adjacent edges, the problem is called adjacent-only minimum quadratic spanning tree. This problem belongs to NP-hard. In this study we investigate the performance of a tabu search algorithm with ejection chain for the bi-objective version of this problem. An experiment with 168 instances is reported.
Investigating How “Good” Characteristics’ Presence are Related with Questions’ Performance
Social query is the practice of sharing questions through collaborative environments. In order to receive help, askers usually broadcast their requests to the entire community. However, the prerequisite to receive help is to have the problem noticed by someone who is able and available to answer. Previous works have identified a correlation between the characteristics of the questions and the outcome of receiving or not an answer. These findings suggest that there are some characteristics that are more likely to attract the helpers´ attention. Our proposal is to analyze the CQA history to identify how these so-called “good” characteristics affect the performance of shared questions. Our results suggest that: (1) answered questions present more of these “good” characteristics than unanswered ones; (2) the more “good” characteristics are present in a question, the more people it attracts; (3) the more people are attracted by a question, the faster it will be answered and more responses will be received; (4) answered questions attract more people than unanswered ones; (5) difficulty does not play a role in attracting people.
Topic Modeling for Short Texts with Co-occurrence Frequency-based Expansion
Short texts are everywhere on the Web, including messages in social media, status messages, etc, and extracting semantically meaningful topics from these collections is an important and difficult task. Topic modeling methods, such as Latent Dirichlet Allocation, were designed for this purpose. However, discovering high quality topics in short text collections is a challenging task. This is because most topic modeling methods rely on information coming from the word co-occurrence distribution in the collection to extract topics. As in short text this information is scarce, topic modeling methods have difficulties in this scenario, and different strategies to tackle this problem have been proposed in the literature. In this direction, this paper introduces a method for topic modeling of short texts that creates pseudo-documents representations from the original documents. The method is simple, effective, and considers word co-occurrence to expand documents, which can be given as input to any topic modeling algorithm. Experiments were run in four datasets and compared against state-of-the-art methods for extracting topics from short text. Results of coherence, NPMI and clustering metrics showed to be statistically significantly better than the baselines in the majority of cases.
A Novel Context-Free Grammar to Guide the Construction of Particle Swarm Optimization Algorithms
Particle Swarm Optimization algorithm (PSO) has been largely studied over the years due to its flexibility and competitive results in different applications. Nevertheless, its performance depends on different aspects of design (e.g., inertia factor, velocity equation, topology). The task of deciding which is the best algorithm design to solve a particular problem is challenging due to the great number of possible variations and parameters to take into account. This work proposes a novel context-free grammar for Grammar-Guided Genetic Programming (GGGP) algorithms to guide the construction of Particle Swarm Optimizers. The proposed grammar addresses four aspects of the PSO algorithm that may strongly influence on its convergence: swarm initialization, neighborhood topology, velocity update equation and mutation operator. To evaluate this approach, a GGGP algorithm was set with the proposed grammar and applied to optimize the PSO algorithm in 32 unconstrained continuous optimization problems. In the experiments, we compared the designs generated considering the proposed grammar with the designs produced by other grammars proposed in the literature to automate PSO designs. The results obtained by the proposed grammar were better than the counterparts. Besides, we also compared the generated algorithms to stateof-art algorithms. The results have shown that the algorithms produced from the grammar achieved competitive results.
A Pareto-based Symbiotic Relationships Model for Unconstrained Continuous Optimization
Symbiotic relationships are one of several phenomena that can be observed in nature. These relationships consist of interactions between organisms and can lead to benefits or damages to those involved. In an optimization context, symbiotic relationships can be used to perform information exchange between populations of candidate solutions to a given problem. This paper presents an information exchange model inspired by symbiotic relationships and applies the model to unconstrained single-objective continuous optimization problems. The symbiotic relationships are modelled using the Pareto dominance criteria inside a computational ecosystem for optimization. The Artificial Bee Colony algorithm is used to compound the populations of the ecosystem. Four models of relationships are analyzed: slavery, competition, altruism and mutualism. Thirty unconstrained single-objective continuous benchmark functions with high number of dimensions (d = 200) are tested and obtained results compared. Results suggest that the proposed model for information exchange favors the balance between exploration and exploitation leading to better results.
A module-based approach for evaluating differential genome-wide expression profiles
Transcription is the process of making an RNA copy of a gene sequence. Next, this copy (mRNA) is then translated into proteins. Proteins dictates the expected behavior inside the cells and are required for the structure, function, and regulation of the body’s tissues and organs. Together, transcription and translation are known as gene expression. Transcriptograms are basically defined as “images” of gene expression data of genomes, by generating expression profiles for transcriptomes. They allow to assess cell metabolism, being capable of discriminating the stage the cell is going through at a given instant, as well as pointing metabolic changes in altered cellular states as compared to a control state, independently of the transcriptome profilling protocol. Though, they cannot highlight differential expression profiles. We present a new possibility of RNA-Seq data analysis using Transcriptograms for discovering module-based differential expression profiles. We demonstrate its practical application while obtaining more specific gene signatures as well as functional annotations, closely related to biomedical context. Moreover, these signatures are also enriched by survival cancer analysis.
An Ontology for Property Crime based on Events from UFO-B Foundational Ontology
Property Crime has been highlighted as one of the major criminal offenses within the Brazilian Legal System. Moreover, it is common that there is some misunderstanding amongst the subtypes of this crime, such as Theft, Robbery, Misappropriation, and Extortion. We still emphasize the very nuance of legal literature that makes this domain as hostile as challenging: a weakly formalized knowledge, the presence of conflicts and ambiguities between norms, the heterogeneity of legal literature, as well as the diversity in reasoning models. Therefore, this article presents, inspired by UFO-B foundational ontology, a conceptual model for the representation of crimes against property in the Brazilian Criminal Code, in order to support some decision-making process, as the agents behavior classification and the inference of punishments. Thus, we present throughout this article, an ontological formalization for the Theory of Crime from Brazilian Penal Code, as well as for Property Crimes applications.
A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets
Imbalanced data sets originating from real world problems, such as medical diagnosis, can be found pervasive. Learning from imbalanced data sets poses its own challenges, as common classifiers assume a balanced distribution of examples’ classes in the data. Sampling techniques overcome the imbalance in the data by modifying the examples’ classes distribution. Unfortunately, selecting a sampling technique together with its parameters is still an open problem. Current solutions include the brute-force approach (try as many techniques as possible), and the random search approach (choose the most appropriate from a random subset of techniques). In this work, we propose a new method to select sampling techniques for imbalanced data sets. It uses Meta-Learning and works by recommending a technique for an imbalanced data set based on solutions to previous problems. Our experimentation compared the proposed method against the brute-force approach, all techniques with their default parameters, and the random search approach. The results of our experimentation show that the proposed method is comparable to the brute-force approach, outperforms the techniques with their default parameters most of the time, and always surpasses the random search approach.
Finding Inference Rules using Graph Mining in Ontological Knowledge Bases
The exponentially grow of Web and data availability, the semantic web area has expanded and each day more data is expressed as knowledge bases. Knowledge bases (KB) used in most projects are represented in an ontology-based fashion, so the data can be better organized and easily accessible. It is common to map these KBs into a graph when trying to induce inference rules from the KB, thus it is possible to apply graph-mining techniques to extract implicit knowledge. One common graph-based task is link prediction, which can be used to predict edges (new facts for the KB) that will appear in a near future. In this paper, we present Graph Rule Learner (GRL), a method designed to extract inference rules from ontological knowledge bases mapped to graphs. GRL is based on graph-mining techniques, and explores the combination of link prediction metrics. Empirical analysis reveled GRL can successfully be applied to NELL(Never-Ending Language Learner)1 helping the system to infer new KB beliefs from existing beliefs (a crucial task for a never-ending learning system).
Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution
Probabilistic logic programming combines logic and probability, so as to obtain a rich modeling language. In this work, we extend PROBLOG, a popular probabilistic logic programming language, with new constructs that allow the representation of (infinite-horizon) Markov decision processes. This new language can represent relational statements, including symmetric and transitive definitions, an advantage over other planning domain languages such as RDDL. We show how to exploit the logic structure in the language to perform Value Iteration. Preliminary experiments demonstrate the effectiveness of our framework.
A proposal for tuning the α parameter in a copula function applied in fuzzy rule-based classification systems
In this paper, we use the concept of extended Choquet integral generalized by a copula function, as proposed by Lucca et al.. More precisely, the copula considered in their study uses a variable α, with different fixed values for testing its behavior. In this contribution we propose a modification of this method assigning a value to this α parameter using a genetic algorithm in order to find the value that best fits it for each class. Specifically, this new proposal is applied in the Fuzzy Reasoning Method (FRM) of Fuzzy Rule-Based Classification Systems (FRBCSs). Finally, we compare the results provided by our new approach against the best solution proposed by Lucca et al. (that uses an fixed value for the variable α). From the obtained results it can be concluded that the genetic learning of the α parameter is statistically superior than the fixed one. Therefore, we demonstrate that our genetic method can be used as an alternative for this function.
Authorship attribution via network motifs identification
Concepts and methods of complex networks can be used to analyse texts at their different complexity levels. Examples of natural language processing (NLP) tasks studied via topological analysis of networks are keyword identification, automatic extractive summarization and authorship attribution. Even though a myriad of network measurements have been applied to study the authorship attribution problem, the use of motifs for text analysis has been restricted to a few works. The goal of this paper is to apply the concept of motifs, recurrent interconnection patterns, in the authorship attribution task. The absolute frequencies of all thirteen directed motifs with three nodes were extracted from the co-occurrence networks and used as classification features. The effectiveness of these features was verified with four machine learning methods. The results show that motifs are able to distinguish the writing style of different authors. In our best scenario, 57.5% of the books were correctly classified. The chance baseline for this problem is 12.5%. In addition, we have found that function words play an important role in these recurrent patterns. Taken together, our findings suggest that motifs should be further explored in other related linguistic tasks.
Knowledge Representation for Argumentation in Agent-Oriented Programming Languages
Argumentation in multi-agent systems provides both a mechanism for agent reasoning under uncertainty and conflicting information as well as for communication in a more elaborate way, allowing agents to understand each other through the exchange of additional information when compared to other forms of agent communication. Even though argumentation techniques can play an important role in multi-agent systems, little research has been carried out on the issues in integrating argumentation techniques and agent-oriented programming languages, which would allow the development of practical applications taking advantage of such combined techniques. In this work, we present an argumentation framework developed on the basis of an agentoriented programming language. We cover mainly the practical aspects of such integration, focusing on the knowledge representation expressivity resulting from it. Our approach allows the development of multi-agent applications where agents are able to use arguments in their decision-making processes as well as for communication. The framework has been successfully used as part of the development of a healthcare multi-agent prototype application.
Learning content from videos is not an easy task and traditional machine learning approaches for computer vision have difficulties in doing it satisfactorily. However, in the past couple of years the machine learning community has seen the rise of deep learning methods that significantly improve the accuracy of several computer vision applications, e.g., Convolutional Neural Networks (ConvNets). In this paper, we explore the suitability of ConvNets for the movie trailers genre classifi- cation problem. Assigning genres to movies is particularly challenging because genre is an immaterial feature that is not physically present in a movie frame, so off-the-shelf image detection models cannot be directly applied to this context. Hence, we propose a novel classification method that encapsulates multiple distinct ConvNets to perform genre classification, namely CoNNECT, where each ConvNet learns features that capture distinct aspects from the movie frames. We compare our novel approach with the current state-of-the-art techniques for movie classification, which make use of well-known image descriptors and lowlevel handcrafted features. Results show that CoNNECT significantly outperforms the state-of-the-art approaches in this task, moving towards effectively solving the genre classification problem.
Co-MLM: a SSL algorithm based on the Minimal Learning Machine
Semi-supervised learning is a challenging topic in machine learning that has attracted much attention in recent years. The availability of huge volumes of data and the work necessary to label all these data are two of the reasons that can explain this interest. Among the various methods for semisupervised learning, the co-training framework has become popular due to its simple formulation and promising results. In this work, we propose Co-MLM, a semi-supervised learning algorithm based on a recently supervised method named Minimal Learning Machine (MLM), built upon co-training framework. Experiments on UCI data sets showed that Co-MLM has promising performance in compared to other co-training style algorithms.
Using the Causal Graph to enhance Translations to solve Contingent Planning Problems
Planning with partial observation, an area called contingent planning, is a complex and challenging problem since it requires to keep track of belief states to search for a contingent plan of actions. Recent approaches considers the agent’s knowledge about the world to compile a contingent planning problem into a full observable planning problem, described in an epistemic logic language, and then use an efficcient full observable planner to solve the translated problem. In this paper we use the concept of relevance and causality to propose a new translation based in a structure called Causal Graph that can improve the belief tracking task of contingent Planning problems described in a more general planning language, in particular problems envolving actions with uncertainty on its conditional effects.
An Accurate Gaussian Process-Based Early Warning System for Dengue Fever
Dengue fever is a mosquito-borne disease present in all Brazilian territory. Brazilian government, however, lacks an accurate early warning system to quickly predict future dengue outbreaks. Such system would help health authorities to plan their actions and to reduce the impact of the disease in the country. However, most attempts to model dengue fever use parametric models which enforce a specific expected behaviour and fail to capture the inherent complexity of dengue dynamics. Therefore, we propose a new Bayesian non-parametric model based on Gaussian processes to design an accurate and flexible model that outperforms previous/standard techniques and can be incorporated into an early warning system, specially at cities from Southeast and Center-West regions. The model also helps understanding dengue dynamics in Brazil through the analysis of the covariance functions generated.
Fault Detection in Hard Disk Drives Based on Mixture of Gaussians
Being able to detect faults in Hard Disk Drives (HDD) can lead to significant benefits to computer manufacturers, users and storage system providers. As a consequence, several works have focused on the development of fault detection algorithms for HDDs. Recently, promising results were achieved by methods using SMART (Self-Monitoring Analysis and Reporting Technology) features and anomaly detection algorithms based on Mahalanobis distance. Nevertheless, the performance of such methods can be seriously degraded when the normality assumption of the data does not hold. As a way to overcome this issue, we propose a new method for fault detection in HDD based on a Gaussian Mixture Model (GMM). The proposed method is tested in a real world dataset and its performance is compared to three other HDD fault detection methods.
Automatic selection of learning bias for active sampling
The classification task, when performed by machine learning algorithms, requires previous training on labeled instances. In many applications, the data labeling process is expensive and can affect the predictive performance of classification models. A current solution has been the use of active learning, which investigates strategies for data labeling. Its main goal is to decide which instances should be labeled and added to the training set, reducing the overall labeling costs. However, the strategy normally depends on a learning algorithm, which should be chosen by a machine learning specialist - usually based on a cross-validation procedure. Consequently, there is a deadlock: without the complete training set, the algorithm that will present the best learning curve cannot be known in advance. Ideally, some type of automatic selection should be employed to solve this deadlock. This study investigates the use of meta-learning for automatic algorithm selection in active learning tasks. Experimental results show that meta-learning is able to find correspondences between algorithms and dataset features in order to help active learning to reduce the risks of incurring in unexpected labeling costs.
Hyper-parameter Tuning of a Decision Tree Induction Algorithm
Supervised classification is the most studied task in Machine Learning. Among the many algorithms used in such task, Decision Tree algorithms are a popular choice, since they are robust and efficient to construct. Moreover, they have the advantage of producing comprehensible models and satisfactory accuracy levels in several application domains. Like most of the Machine Leaning methods, these algorithms have some hyperparameters whose values directly affect the performance of the induced models. Due to the high number of possibilities for these hyper-parameter values, several studies use optimization techniques to find a good set of solutions in order to produce classifiers with good predictive performance. This study investigates how sensitive decision trees are to a hyper-parameter optimization process. Four different tuning techniques were explored to adjust J48 Decision Tree algorithm hyper-parameters. In total, experiments using 102 heterogeneous datasets analyzed the tuning effect on the induced models. The experimental results show that even presenting a low average improvement over all datasets, in most of the cases the improvement is statistically significant.
Extreme Risk Averse Policy for Goal-Directed Risk-Sensitive Markov Decision Process
The Goal-Directed Risk-Sensitive Markov Decision Process allows arbitrary risk attitudes for the probabilistic planning problem to reach a goal state. In this problem, the risk attitude is modeled by an expected exponential utility and a risk factor λ. However, the problem is not well defined for every λ, posing the problem of defining the maximum (extreme) value for this factor. In this paper, we propose an algorithm to find this -extreme risk factor and the corresponding optimal policy.
Improved Airport Ground Traffic Control with Domain-Dependent Heuristics
In this paper we study the application of a domaindependent heuristic to airport ground traffic control. We consider two variants of the problem. In the first, proposed for the International Planning Competition in 2004, the in-bound and out-bound airplanes have fixed parking and take-off positions. In the second, more realistic variant a controller can assign dynamically for each airplane the runway for take-off or the parking position, such that the total movement of planes at the airport is minimized. We are particularly interested in the second variant, which has an implicitly defined goal state where multiple states could satisfy the goal condition, and the impact of this fact on domain-independent and domain-dependent heuristics. We compare domain-independent heuristics in the Fast Downward planner on this domain to a domain-dependent heuristic.
In this paper we study the application of pattern databases (PDBs) to optimally solving Atomix. Atomix is a puzzle, where one has to assemble a molecule from atoms by sliding moves. It is particularly challenging, because the slides makes it hard to create admissible heuristics, and state-of-theart heuristics are rather uninformed. A pattern database (PDB) stores solutions to an abstract version of a state space problem. An admissible lower bound for a given state is obtained by decomposing it into abstract states and combining their precomputed solutions. Different from other puzzles a pattern in Atomix cannot be simply obtained by omitting pieces from the puzzle. We also study the search algorithm Partial Expansion A∗’s application to Atomix, as a reduced-memory alternative to A∗. Experiments show our method solves more instances and significantly improves current lower bounds, running times and node expansions compared to the best solution in the literature.
Determining the Structure of Decision Directed Acyclic Graphs for Multiclass Classification Problems
An usual strategy to solve multiclass classification problems in Machine Learning is to decompose them into multiple binary sub-problems. The final multiclass prediction is obtained by a proper combination of the outputs of the binary classifiers induced in their solution. Decision directed acyclic graphs (DDAG) can be used to organize and to aggregate the outputs of the pairwise classifiers from the one-versus-one (OVO) decomposition. Nonetheless, there are various possible DDAG structures for problems with many classes. In this paper evolutionary algorithms are employed to heuristically find the positions of the OVO binary classifiers in a DDAG. The objective is to place easier sub-problems at higher levels of the DDAG hierarchical structure, in order to minimize the occurrence of cumulative errors. For estimating the complexity of the binary sub-problems, we employ two indexes which measure the separability of the classes. The proposed approach presented sound results in a set of experiments on benchmark datasets, although random DDAGs also performed quite well.
A Voronoi Diagram Based Classifier for Multiclass Imbalanced Data Sets
The imbalance problem is receiving an increasing attention in the literature. Studies in binary cases are recurrent, however there still are several real world problems with more than two classes. The known solutions for binary datasets may not be applicable in this case. Some efforts are being applied in decomposition techniques which transforms a multiclass problem into some binary problems. However it is also possible to face a multiclass problem with an ad hoc approach, i.e., a classifier able to handle all classes at once. In this work a method able to handle several classes is proposed. This new method is based on the Voronoi diagram. We try to dynamically divide the feature space into several regions, each one assigned to a different class. It is expected for the method to be able to construct a complex classification model. However, as it is in its beginning, some tests need to be performed in order to evaluate its feasibility. Experiments with some classical classifiers confirm its feasibility, and comparisons with ad hoc methods found in literature show its potentiality.
Evaluating Binary Encoding Techniques for WiSARD
Many weightless neural networks, such as WiSARD, are RAM-based classifiers that receive binary data as input. In order to convert raw data into binary input, several techniques are applicable. This work evaluates the impact of some of these binarization techniques on the accuracy of two types of classifiers: WiSARD model and WiSARD with bleaching mechanism. The binary encoding techniques explored were: (i) thermometer, (ii) threshold, (iii) local threshold, (iv) Marr-Hildreth filter, and (v) Laplacian filter. The MNIST digit dataset was used to compare the accuracy obtained by each encoding technique. Results showed a difference of more than 20% in the accuracy due to the choice of encoding approach.
Extreme Learning Machines for Datasets with Missing Values Using the Unscented Transform
The existence of missing data is a common fact in real applications which can significantly affect the data analysis process. In order to overcome this problem, many methods have been proposed in the literature. Extreme Learning Machine (ELM) has become a very popular research topic in machine learning and artificial intelligence areas due to its characteristics such as fast training procedure, good generalization and universal approximation capability. Although ELM has been successfully applied in different domains, its basic formulation cannot handle datasets with missing values properly. This paper presents a variant of the Extreme Learning Machine (ELM) for datasets with missing values. In the proposed method, probability distributions for the missing values are estimated using the expectation maximization (EM) algorithm, assuming that data is normally distributed. The Unscented Transform (UT) is used to estimate the values of the hidden layer outputs, and the weights of the output layer are assigned using the Moore-Penrose Pseudoinverse. Numerical experiments are carried out in order to evaluate the performance of the proposed method in four real world and two synthetic regression datasets. The results show that the proposed method presented a good performance in terms of Average RootMean-Squared Error (ARMSE).
Image-Set Matching by Two Dimensional Generalized Mutual Subspace Method
In this paper, we present a novel supervised learning algorithm for object recognition from sets of images, where the sets describe most of the variation in an object’s appearance caused by lighting, pose and view angle. In this scenario, generalized mutual subspace method (gMSM) has attracted attention for image-set matching due to its advantages in accuracy and robustness. However, gMSM employs PCA, which has high computational cost contrasting to state-of-art appearancebased methods. To create a faster method, we replace the traditional PCA by 2D-PCA and variants on gMSM framework. In general, 2D-PCA and variants require less memory resource than conventional PCA since its covariance matrix is calculated directly from two-dimensional matrices. The introduced method has the advantage of representing the subspaces in a more compact manner, providing reasonably competitive recognition rate comparing to the traditional MSM, confirming the suitability of employing 2D-PCA and variants on gMSM framework. These results have been revealed through experimentation conducted on five widely used datasets.
Towards Knowledge Transfer in Deep Reinforcement Learning
Driven by recent developments in the area of Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The technology is termed Deep Reinforcement Learning (DRL) and combines the classic field of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. DRL enables solutions for difficult and high dimensional tasks, such as Atari game playing, for which previously proposed RL methods wereinadequate. However, these new olution approaches still take a long time to learn how to actuate in such domains and so farare mainly researched for single task cenarios. The ability to generalize gathered knowledge and transfer it to another task has been researched for classical RL, but remains an open problem for the DRL domain. Consequently, in this article we evaluate under which conditions the application of Transfer Learning (TL) to the DRL domain improves the learning of a new task. Our results indicate that TL can greatly accelerate DRL when transferring knowledge from similar tasks, and that the similarity between tasks plays a key role in the success or failure of knowledge transfer.
Image Classification Using Sum-Product Networks for Autonomous Flight of Micro Aerial Vehicles
Flying autonomous micro aerial vehicles (MAVs) in indoor environments is still a challenging task, as MAVs are not capable of carrying heavy sensors as Lidar or RGD-B, and GPS signals are not reliable indoors. We investigate a strategy where image classification is used to guide a MAV; one of the main requirements then is to have a classifier that can produce results quickly during operation. The goal here is to explore the performance of Sum-Product Networks and Arithmetic Circuits as image classifiers, because these formalisms lead to deep probabilistic models that are tractable during operation. We have trained and tested our classifiers using the Libra toolkit and real images. We describe our approach and report the result of our experiments in the paper.
SSDP: A Simple Evolutionary Approach for Top-K Discriminative Patterns in High Dimensional Databases
It is a great challenge to companies, governments and researchers to extract knowledge in high dimensional databases. Discriminative Patterns (DPs) is an area of data mining that aims to extract relevant and readable information in databases with target attribute. Among the algorithms developed for search DPs, it has highlighted the use of evolutionary computing. However, the evolutionary approaches typically (1) are not adapted for high dimensional problems and (2) have many nontrivial parameters. This paper presents SSDP (Simple Search Discriminative Patterns), an evolutionary approach to search the top-k DPs adapted to high dimensional databases that use only two easily adjustable external parameters.
Musical Scales Recognition via Deterministic Walk in a Graph
Musical scales play an important role in melodies, since its properties are reflected to the melodic essence. The extraction and understanding of scales are essential in both analysis and composition of music. However, the scale identification is a nontrivial task. Consequently, classic algorithms for identifying scales have been developed based on the most popular scales, such as major and minor scales. In this paper, we propose a comprehensive method for identifying musical scales, which allows to detect a wide range of scales beyond the traditional ones. Our method uses a deterministic walk through the nodes of a graph, where each node represents a valid interval structure. The transition between nodes is performed following a validation rule that governs the fragmentation of intervals. Moreover, if the scale is incomplete, possible structures can be determined and the scale is estimated according to the harmonic similarity percentage measure. The proposed method has been tested using a database of Finnish folk melodies and a data set of random melodies composed using rarely used scales. Experimental results show good performance of the proposed technique.
No-Reference Image Quality Assessment Using Texture nformation Banks
In this paper, we propose a new no-reference quality assessment ethod which uses a machine learning technique based on texture analysis. The proposed method compares test images with texture images of a public database. Local Binary Patterns (LBPs) are used as local texture feature descriptors. With a Csiszar-Morimoto divergence measure, the histograms of ´ the LBPs of the test images are compared with the histograms of the LBPs of the database texture images, generating a set of difference measures. These difference measures are used to blindly predict the quality of an image. Experimental results show that the proposed method is fast and has a good quality prediction power, outperforming other no-reference image quality assessment methods.
Investigating selection strategies in multi-objective probabilistic model based algorithms
Recent advances on multi-objective evolutionary algorithm (MOEAs) have acknowledged the important role played by selection, replacement, and archiving strategies in the behavior of these algorithms. However, the influence of these methods has been scarcely investigated for the particular class of MOEAs that use probabilistic modeling of the solutions. In this paper we fill this void by proposing an analysis of the role of the aforementioned strategies on an extensive set of bi-objective functions. We focus on the class of algorithms that use Gaussian univariate marginal models, and study how typical selection and replacement strategies used together with this probabilistic model impact the behavior of the search. Our analysis is particularized for a set of bi-objective functions that exhibit a representative set of characteristics (e.g. decomposable, ill-conditioned, non-linear, etc.). The experimental results shows that MOEAs that use simple probabilistic modeling outperform traditional MOEAs based on crossover operators.
Tree-based Grammar Genetic Programming to Evolve Particle Swarm Algorithms
Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-guided Genetic Programming algorithms (GGGP), in special, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar; differently from methods that simply select designs in a pre-defined limited search space. In this work, we proposed a tree-based GGGP technique for the generation of PSO algorithms. This paper intends to investigate whether this approach can improve the production of PSO algorithms when compared to other GGGP techniques already used to solve the current problem. In the experiments, a comparison between the tree-based and the commonly used linearized GGGP approach for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-art optimization algorithms, and the results showed that the algorithms produced by the tree-based GGGP achieved competitive results.
Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains
Although Reinforcement Learning methods have successfully been applied to increasingly large problems, scalability remains a central issue. While Object-Oriented Markov Decision Processes (OO-MDP) are used to exploit regularities in a domain, Multiagent System (MAS) methods are used to divide workload amongst multiple agents. In this work we propose a novel combination of OO-MDP and MAS, called Multiagent Object-Oriented Markov Decision Process (MOO-MDP), so as to accrue the benefits of both strategies and be able to better address scalability issues. We present an algorithm to solve deterministic cooperative MOO-MDPs, and prove that it learns optimal policies while reducing the learning space by exploiting state abstractions. We experimentally compare our results with earlier approaches and show advantages with regard to discounted cumulative reward, number of steps to fulfill the task, and Q-table size.
Using Preferences over Sources of Information in Argumentation-Based Reasoning
Argumentation-based reasoning plays an important role in agent reasoning and communication, yet little research has been carried out on the issues in integrating argumentation techniques into practical multi-agent platforms and the various sources of information in such systems. In this work, we extend an argumentation-based reasoning mechanism to take into account preferences over arguments supporting contrary conclusions, which in practice means the agent will be able to act more informedly, being able to decide on beliefs about which it would be otherwise ambivalent. Such preferences come from elements that are present or can be more easily obtained in the context of practical multi-agent programming platforms, such as multiple sources from which the information (used to construct the arguments) was acquired, as well as varying degrees of trust on them. Further, we introduce different agent profiles by varying the way certain operators are applied over the various information sources leading to the preferences over competing arguments in our approach. Unlike previous approaches, our approach accounts for multiple sources for a single piece of information and is based on an argumentation-based reasoning mechanism implemented on a multi-agent platform so arguably more computationally grounded than those approaches.