Sistem Cerdas Permainan Papan The Battle Of Honor dengan Decision Making dan Machine Learning

Abstract. Intelligent System of the Battle of Honor Board Game with Decision Making and Machine Learning. The Battle of Honor is a board game where 2 players face each other to bring down their opponent's flag. This game requires a third party to act as the referee because the players cannot see each other's pawns during the game. The solution to this is to implement Rule-Based Systems (RBS) on a system developed with Unity to support the referee's role in making decisions based on the rules of the game. Researchers also develop Artificial Intelligence (AI) as opposed to applying Case-Based reasoning (CBR). The application of CBR is supported by the nearest neighbor algorithm to find cases that have a high degree of similarity. In the basic test, the results of the CBR test were obtained with the highest formulated accuracy of the 3 examiners, namely 97.101%. In testing the AI scenario as a referee, it is analyzed through colliding pieces and gives the right decision in determining victoryKeywords: The Battle of Honor, CBR, RBS, unity, AIAbstrak. The Battle of Honor merupakan permainan papan dimana 2 pemain saling berhadapan untuk menjatuhkan bendera lawannya. Permainan ini membutuhkan pihak ketiga yang berperan sebagai wasit karena pemain yang saling berhadapan tidak dapat saling melihat bidak lawannya. Solusi dari hal tersebut yaitu mengimplementasikan Rule-Based Systems (RBS) pada sistem yang dikembangkan dengan Unity untuk mendukung peran wasit dalam memberikan keputusan berdasarkan aturan permainan. Peneliti juga mengembangkan Artificial Intelligence (AI) sebagai lawan dengan menerapkan Case-Based reasoning (CBR). Penerapan CBR didukung dengan algoritma nearest neighbour untuk mencari kasus yang memiliki tingkat kemiripan yang tinggi. Pada pengujian dasar didapatkan hasil uji CBR dengan accuracy yang dirumuskan tertinggi dari 3 penguji yaitu 97,101%. Pada pengujian skenario AI sebagai wasit dianalisis lewat bidak yang bertabrakan dan memberikan keputusan yang tepat dalam menentukan kemenangan.Kata Kunci: The Battle of Honor, CBR, RBS, unity, A

Research and development talents training in China universities - based on the consideration of education management cost planning

Research and development (R&D) talents training are asymmetric in China universities and can be of great significance for economic and social sustainable development. For the purpose of making an in-depth analysis in the education management costs for R&D talents training, the belief rule-based (BRB) expert system with data increment and parameter learning is developed to achieve education management cost prediction for the first time. In empirical analysis, based on the BRB expert system, the past investments and future planning of education management costs are analyzed using real education management data from 2001 to 2019 in 31 Chinese provinces. Results show that: (1) the existing education management cost investments have a significant regional difference; (2) the BRB expert system has excellent accuracy over some existing cost-prediction models; and (3) without changing the current education management policy and education cost input scheme, the regional differences in China’s education management cost input always exist. In addition to the results, the present study is helpful for providing model supports and policy references for decision makers in making well-grounded plans of R&D talents training at universitie

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.Funding for open access charge: Universidad de Granada / CBUA.ERDF and the Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20)ERDF and Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711)Spanish Ministry of Science and Innovation (grant number PID2019-107793GB-I00

Evidential reasoning for preprocessing uncertain categorical data for trustworthy decisions: An application on healthcare and finance

The uncertainty attributed by discrepant data in AI-enabled decisions is a critical challenge in highly regulated domains such as health care and finance. Ambiguity and incompleteness due to missing values in output and input attributes, respectively, is ubiquitous in these domains. It could have an adverse impact on a certain unrepresented set of people in the training data without a developer's intention to discriminate. The inherently non-numerical nature of categorical attributes than numerical attributes and the presence of incomplete and ambiguous categorical attributes in a dataset increases the uncertainty in decision-making. This paper addresses the challenges in handling categorical attributes as it is not addressed comprehensively in previous research. Three sources of uncertainties in categorical attributes are recognised in this research. The informational uncertainty, unforeseeable uncertainty in the decision task environment, and the uncertainty due to lack of pre-modelling explainability in categorical attributes are addressed in the proposed methodology on maximum likelihood evidential reasoning (MAKER). It can transform and impute incomplete and ambiguous categorical attributes into interpretable numerical features. It utilises a notion of weight and reliability to include subjective expert preference over a piece of evidence and the quality of evidence in a categorical attribute, respectively. The MAKER framework strives to integrate the recognised uncertainties in the transformed input data that allow a model to perceive data limitations during the training regime and acknowledge doubtful predictions by supporting trustworthy pre-modelling and post modelling explainability. The ability to handle uncertainty and its impact on explainability is demonstrated on a real-world healthcare and finance data for different missing data scenarios in three types of AI algorithms: deep-learning, tree-based, and rule-based model