Kim On Chin

Ontology-based query expansion for supporting information retrieval in agriculture

The demand for relevant knowledge related to effective and efficient agricultural development has increased tremendously recently in all over the world. The web can be considered as a distributed mass of simple hypertext pages and this gives rise not only to the redundancy of information but also difficulty in managing the relationship among the concepts of information. Thus, a formal way to represent knowledge on agricultural development is crucial. Information related to agricultural development can be represented using ontological modeling that enables the integration of knowledge obtained from heterogeneous sources. Despite this fact, the semantic interpretation of users’ information needs become crucial in retrieval mechanisms. One of the successful techniques used to ensure relevant information is obtained, is to expand the input query by employing the query-related terms derived from the ontology in order to approximate the actual user’s intention. This paper reviews various relevant researches conducted in ontology-based query expansion, particularly in agriculture domain.

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.

Rule-Based Sentiment Analysis for Financial News

This paper describes a rule-based sentiment analysis algorithm for polarity classification of financial news articles. The system utilizes a prior polarity lexicon to classify the financial news articles into positive or negative. Sentiment composition rules are used to determine the polarity of each sentence in the news article, while the Positivity/Negativity ratio (P/N ratio) is used to calculate the sentiment values of the overall content of each news article. The performance of the Sentiment Analyser was evaluated using a dataset of manually annotated financial news articles collected from various online financial newspapers. The result was encouraging as our Sentiment Analyser obtained an overall F-Score of 75.6% for both positive and negative classifications.

A robust framework for web information extraction and retrieval

The large volume of online and offline information that is available today has overwhelmed users’ efficiency and effectiveness in processing this information in order to extract relevant information. The exponential growth of the volume of Internet information complicates information access. Thus, it is a very time consuming and complex task for user in accessing relevant information. Information retrieval (IR) is a branch of artificial intelligence that tackles the problem of accessing and retrieving relevant information. The aim of IR is to enable the available data source to be queried for relevant information efficiently and effectively. This paper describes a robust information retrieval framework that can be used to retrieve relevant information. The proposed information retrieval framework is designed to assist users in accessing relevant information effectively and efficiently as it handles queries based on user preferences. Each component and module involved in the proposed framework will be explained in terms of functionality and the processes involved.

Impact of financial news headline and content to market sentiment

Business and financial news are important resources that investors referred to when monitoring the stock performance. News brings us the latest information about the stock market. Studies have shown that business and financial news have a strong correlation with future stock performance. Business and financial news can be used to extract sentiments and opinions that may assist in the stock price predictions. In this paper, we present a sentiment analyser for financial news articles using lexicon-based approach. We utilized two most important elements of news, the headline and the content as our test data. We use polarity lexicon to distinguish between positive and negative polarity of each term in the corpus. We further investigate on how news headline will affect the sentiment analysis by adjusting the weights of the news headline and news content’s sentiment value. Three sets of experiments were carried out using headline only, content only and headline and content as test data. In the experiment, we used non-stemming tokens and stemming tokens when considering individual word found in the news article. The preliminary results are presented and discussed in this paper.

Automated Evaluation for AI Controllers in Tower Defense Game Using Genetic Algorithm

This paper presents the research result of implementing evolutionary algorithms towards computational intelligence in Tower Defense game (TD game). TD game is a game where player(s) need to build tower to prevent the creeps from reaching their based. Penalty will be given if player losses any creeps during gameplays. It is a suitable test bed for planning, designing, implementing and testing either new or modified AI techniques due to the complexity and dynamicity of the game. In this research, Genetic Algorithm (GA) will be implemented to the game with two different neural networks: (1) Feed- forward (FFNN) and (2) Elman Recurrent (ERNN) used as tuner of the weights. ANN will determine the placement of the towers and the fitness score will be calculated at the end of each game. As a result, it is proven that the implementation of GA towards FFNN is better compared to GA towards ERNN.

Pareto Ensembles for Evolutionary Synthesis of Neurocontrollers in a 2D Maze-Based Video Game

In this paper, we present a study of evolving artificial neural network controllers for autonomously playing maze-based video game. A system using multi-objective evolutionary algorithm is developed, which is called as Pareto Archived Evolution Strategy Neural Network (PAESNet), with the attempt to find a set of Pareto optimal solutions by simultaneously optimizing two conflicting objectives. The experiments are designed to address two research aims investigating: (1) evolving weights (including biases) of the connections between the neurons and structure of the network through multi-objective evolutionary algorithm in order to reduce its runtime operation and complexity, (2) improving the generalization ability of the networks by using neural network ensemble model. A comparative analysis between the single network model as the baseline system and the model built based on the neural ensemble are presented. The evidence from this study suggests that Pareto multi-objective paradigm and neural network ensembles can be effective for creating and controlling the behaviors of video game characters.

Enrichment of BOW Representation with Syntactic and Semantic Background Knowledge

The basic Bag of Words (BOW) representation, that is generally used in text documents clustering or categorization, loses important syntactic and semantic information contained in the documents. When the text document contains a lot of stop words or when they are of a short length this may be particularly problematic. In this paper, we study the contribution of incorporating syntactic features and semantic knowledge into the representation in clustering texts corpus. We investigate the quality of clusters produced when incorporating syntactic and semantic information into the representation of text documents by analyzing the internal structure of the cluster using the Davies- Bouldin (DBI) index. This paper studies and compares the quality of the clusters produced when four different sets of text representation used to cluster texts corpus. These text representations include the standard BOW representation, the standard BOW representation integrated with syntactic features, the standard BOW representation integrated with semantic background knowledge and finally the standard BOW representation integrated with both syntactic features and semantic background knowledge. Based on the experimental results, it is shown that the quality of clusters produced is improved by integrating the semantic and syntactic information into the standard bag of words representation of texts corpus.

An evolutionary multi-objective optimization approach to computer go controller synthesis

Evolutionary multi-objective optimization (EMO) has gained popularity and it has been successfully applied in several research areas. Based on the literature review conducted, EMO approach has not been applied in any Go game application. In this study, artificial neural networks (ANNs) are evolved with an EMO algorithm, Pareto Archived Evolution Strategies (PAES) for computer player to learn and play the 7x7 board Go game against GNU Go. In this study, two conflicting objectives are investigated: first, maximize the ability of neural player to play the Go game and second, minimize the complexity of the ANN by reducing the hidden units. Several comparative empirical experiments were conducted that showed EMO which optimize two distinct and conflicting objectives outperformed the single-objective (SO) optimization which only optimized the first objective with no pressure selection on the second objective.

Comparing the performance of deterministic dynamic adaptation GA and self adaptive GA in online auctions environment

The proliferation of online auctions has caused the increasing need to monitor and track multiple bids in multiple auctions. As a solution to the problem, an autonomous agent was developed to work in a flexible and configurable heuristic decision making framework that can tackle the problem of bidding across multiple auctions that apply different protocols (English, Vickrey and Dutch). Due to the dynamic and unpredictable nature of online auctions, the agent utilizes genetic algorithm to search for effective solution. Instead of using the conventional genetic algorithm, this paper investigates the application of deterministic dynamic adaptation genetic algorithm and self adaptive genetic algorithm to search for the most effective strategies (offline). An empirical evaluation on the comparison between the effectiveness of self-adaptive genetic algorithm and deterministic dynamic adaptation genetic algorithm for searching the most effective strategies in the online auction environment are discussed in this paper.