A simple hybrid algorithm for improving team sport AI

In the very popular genre of team sports games defeating the opposing AI is the main focus of the gameplay experience. However the overall quality of these games is significantly damaged because, in a lot of cases, the opposition is prone to mistakes or vulnerable to exploitation. This paper introduces an AI system which overcomes this failing through the addition of simple adaptive learning and prediction algorithms to a basic ice hockey defence. The paper shows that improvements can be made to the gameplay experience without overly increasing the implementation complexity of the system or negatively affecting its performance. The created defensive system detects patterns in the offensive tactics used against it and changes elements of its reaction accordingly; effectively adapting to attempted exploitation of repeated tactics. This is achieved using a fuzzy inference system that tracks player movement, which greatly improves variation of defender positioning, alongside an N-gram pattern recognition-based algorithm that predicts the next action of the attacking player. Analysis of implementation complexity and execution overhead shows that these techniques are not prohibitively expensive in either respect, and are therefore appropriate for use in games

N-gram Based Text Categorization Method for Improved Data Mining

Though naïve Bayes text classifiers are widely used because of its simplicity and effectiveness, the techniques for improving performances of these classifiers have been rarely studied. Naïve Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability of features belonging to a class, which the features are selected by feature selection methods. However, its performance is often imperfect because it does not model text well, and by inappropriate feature selection and some disadvantages of the Naive Bayes itself. Sentiment Classification or Text Classification is the act of taking a set of labeled text documents, learning a correlation between a document’s contents and its corresponding labels and then predicting the labels of a set of unlabeled test documents as best as possible. Text Classification is also sometimes called Text Categorization. Text classification has many applications in natural language processing tasks such as E-mail filtering, Intrusion detection systems, news filtering, prediction of user preferences, and organization of documents. The Naive Bayes model makes strong assumptions about the data: it assumes that words in a document are independent. This assumption is clearly violated in natural language text: there are various types of dependences between words induced by the syntactic, semantic, pragmatic and conversational structure of a text. Also, the particular form of the probabilistic model makes assumptions about the distribution of words in documents that are violated in practice. We address this problem and show that it can be solved by modeling text data differently using N-Grams. N-gram Based Text Categorization is a simple method based on statistical information about the usage of sequences of words. We conducted an experiment to demonstrate that our simple modification is able to improve the performance of Naive Bayes for text classification significantly. Keywords: Data Mining, Text Classification, Text Categorization, Naïve Bayes, N-Grams

Analysis of customer satisfaction using surveys with open questions

In this paper the use of open-ended questionnaires to improve the evaluation of customer satisfaction according to ISO 9001 in small and medium-sized enterprises is analyzed. By obtaining more information in comparison to the closed questions questionnaire some limitations coming from the second one are removed. The open-ended questionnaire is analyzed by applying a semantic study to obtain the root of each word and remove the word that is not relevant for the information needs of the organization. This way the positive or negative trend for each response is identified. This study proofs that the use of open-ended questionnaires facilitates the fulfilment of the ISO 9001 standard. It allows the comparison between the data coming from the Customer Relationship Management System (CRM) and the data obtained through the questionnaire. Furthermore it opens new areas of research based in the use of semantic analysis in quality systems and marketing

Study of a New Chaotic Dynamical System and Its Usage in a Novel Pseudorandom Bit Generator

A new chaotic discrete dynamical system, built on trigonometric functions, is proposed. With intent to use this system within cryptographic applications, we proved with the aid of specific tools from chaos theory (e.g., Lyapunov exponent, attractor’s fractal dimension, and Kolmogorov-Smirnov test) and statistics (e.g., NIST suite of tests) that the newly proposed dynamical system has a chaotic behavior, for a large parameter’s value space, and very good statistical properties, respectively. Further, the proposed chaotic dynamical system is used, in conjunction with a binary operation, in the designing of a new pseudorandom bit generator (PRBG) model. The PRBG is subjected, by turns, to an assessment of statistical properties. Theoretical and practical arguments, rounded by good statistical results, confirm viability of the proposed chaotic dynamical system and newly designed PRBG, recommending them for usage within cryptographic applications

Comparing dynamitic difficulty adjustment and improvement in action game

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master ResearchDesigning a game difficulty is one of the key things as a game designer. Player will be feeling boring when the game designer makes the game too easy or too hard. In the past decades, most of single player games can allow players to choose the game difficulty either easy, normal or hard which define the overall game difficulty. In action game, these options are lack of flexibility and they are unsuitable to the player skill to meet the game difficulty. By using Dynamic Difficulty Adjustment (DDA), it can change the game difficulty in real time and it can match different player skills. In this paper, the final goal is the comparison of the three DDA systems in action game and apply an improved DDA. In order to apply a new improved DDA, this thesis will evaluate three chosen DDA systems with chosen action decision based AI for action game. A new DDA measurement formula is applied to the comparing section

Exploiting N-Gram Analysis to Predict Operator Sequences

N-gram analysis provides a means of probabilistically predicting the next item in a sequence. Due originally to Shannon, it has proven an effective technique for word prediction in natural language processing and for gene sequence analysis. In this paper, we investigate the utility of n-gram analysis in predicting operator sequences in plans. Given a set of sample plans, we perform n-gram analysis to predict the likelihood of subsequent operators, relative to a partial plan. We identify several ways in which this information might be integrated into a planner. In this paper, we investigate one of these directions in further detail. Preliminary results demonstrate the promise of n-gram analysis as a tool for improving planning performance