Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization

This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. The generated controllers are evaluated on its performances based on Pareto analysis. Furthermore, the generated controllers are tested with four different environments particularly for robustness assessment. The testing environments are different from the environment in which evolution was conducted. Interestingly however, the testing results showed some of the mobile robots are still robust to the testing environments. The controllers allowed the robots to home in towards the signal source with different movements’ behaviors. This study has thus revealed that the PDE-EMO algorithm can be practically used to automatically generate robust controllers for RFlocalization behavior in autonomous mobile robots

An evolutionary based features construction methods for data summarization approach

Coral reefs are on course to become the first ecosystem that human activity will eliminate entirely from the Earth, a leading United Nations scientist claims. It is predicted that this event will occur before the end of the present century, which means that there are children already born who will live to see a world without coral. Coral reefs are important for the immense biodiversity of their ecosystems. They contain a quarter of all marine species. This research addresses the question whether a data summarization approach can be utilized to predict the survival of Coral Reefs in Malaysia by identifying the survival factors for these Coral Reefs. A data summarization approach is proposed due to its capability to learn data stored in multiple tables. In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). The DARA algorithm will be applied to summarize data stored in the non-target tables by clustering them into groups, where multiple records stored in non­target tables correspond to a single record i,tored in a target table. Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. These methods are proposed to improve the descriptive accuracy of the summarized data. In the process of summarizing relational data, a genetic algorithm is also applied and several feature scoring measures are evaluated in order to find the best set of relevant constructed features. In this work, we empirically compare the predictive accuracies of classification tasks based on the proposed feature construction methods and also the existing feature construction methods. The experimental results show that the predictive accuracy of classifying data that are summarized based on VLFCWS method using Total Cluster Entropy combined with Information Gain (CE-JG) as feature scoring outperforms in most cases

The customer choice model of commercial retailers based on MarKov analysis

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity

An intelligent categorization tool for malay research articles

Unlabeled research articles published in Malay language are becoming increas­ ingly common and available in Malaysia. Thus, the task of manually indexing these research articles is difficult and time consuming. In order to facilitate research activities that depend on research resources written in l\lalay language, these research articles must be categorized or indexed efficiently so that appro­ priate and relevant domains of knowledge can be recommended to researchers in l\falaysia. There are not many researches conducted to efficiently categorize Malay research articles. The task of categorizing Malay research articles is more complex compared to the task of categorizing English research articles due to the complexity of Malay language and thus categorizing Malay research articles represents a major contemporary challenge. Malay text documents are often represented as high-dimensional and sparse vectors, by using Malay words as features, which consist of a few thousand dimensions and a sparsity of 95 to 99% is typical. Determining the appropriate number of categories for large amount of Malay documents is also difficult and time consuming task due to the sparsity of the documents. Related documents may be grouped into different clusters, if there are too many number of categories assigned to these documents. On the other hand, unrelated documents may be clustered into the same cluster, if there are too few number of categories assigned to these documents. This research ad­dresses issues that involve improving several pre-processing processes that affect the performance of the clustering process. These pre-processing processes include stemming, part-of-speech tagging and named-entity recognition. In this work, the effects of improving all these pre-processing processes will be investigated. It is anticipated that by improving the clustering results, it will also improve the mapping of Malay and English clusters obtained from the bilingual clustering. Hence, by increasing the mapping percentage for the bilingual clusters, a more robust clustering algorithm can be developed for clustering bilingual documents. As a result, by increasing the mapping percentage for the bilingual clusters, a more robust clustering algorithm can be developed for clustering bilingual documents. In this study, a genetic algorit.hm {GA) is also proposed to be implemented in order to determine the set of terms that can be used in clustering bilingual documents with more effective

Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man

The objective of this study is to focus on the automatic generation of game artificial intelligence (AI) controllers for Ms. Pac-Man agent by using artificial neural network (ANN) and multiobjective artificial evolution. The Pareto Archived Evolution Strategy (PAES) is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing Ms. Pac-Man scores (screen-capture mode) and minimizing neural network complexity. This proposed algorithm is called Pareto Archived Evolution Strategy Neural Network or PAESNet. Three different architectures of PAESNet were investigated, namely, PAESNet with fixed number of hidden neurons (PAESNet_F), PAESNet with varied number of hidden neurons (PAESNet_V), and the PAESNet with multiobjective techniques (PAESNet_M). A comparison between the single- versus multiobjective optimization is conducted in both training and testing processes. In general, therefore, it seems that PAESNet_F yielded better results in training phase. But the PAESNet_M successfully reduces the runtime operation and complexity of ANN by minimizing the number of hidden neurons needed in hidden layer and also it provides better generalization capability for controlling the game agent in a nondeterministic and dynamic environment

Semantic agent architecture: embedding ontology into the agent's reasoning engine

This research presents the development of semantic agent architecture which incorporates semantic technology that allows decision making, reasoning and learning. The agent architecture is the software architecture that is intended to support decision making process for intelligent agent. From the review of existing agent architectures, BDI architecture is chosen to incorporate semantic technology due to the widely adoption of BDI architecture. The BDI architecture is based on the practical reasoning and mentalistic notion. Semantic technology is set of technologies that make data more easily machine-processable. Thus, by incorporating semantic technology into BDI architecture, a semantic agent architecture that allows decision making, reasoning and learning is created. This study illustrates the semantic agent architecture through simple trading system. The trust and reputation are augmented into the agent architecture to allow the agent to evaluate the performance of the other agent

Artificial Neural Controller Synthesis in Autonomous Mobile Cognition

This paper describes a new approach in using multi-objective evolutionary algorithms in evolving the neural network that acts as a controller for the phototaxis and radio frequency localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is utilized to generate the Pareto optimal sets through a 3-layer feed-forward artificial neural network that optimize the conflicting objectives of robot behavior and network complexity, where the two different types of robot behaviors are phototaxis and RF-localization, respectively. Thus, there are two fitness functions proposed in this study. The testing results showed the robot was able to track the light source and also home-in towards the RF-signal source successfully. Furthermore, three additional testing results have been incorporated from the robustness perspective: different robot localizations, inclusion of two obstacles, and moving signal source experiments, respectively. The testing results also showed that the robot was robust to these different environments used during the testing phases. Hence, the results demonstrated that the utilization of the evolutionary multi-objective approach in evolutionary robotics can be practically used to generate controllers for phototaxis and RF-localization behaviors in autonomous mobile robot