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2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment. [ABSTRACT FROM AUTHOR]

League of Legends (LoL) has been on a rise in becoming an extremely favored multiplayer online battle arena (MOBA) game ever since its release in 2009. This paper presents a logic mining technique to model the results (Win / Lose) of the LoL games played in the professional league of South Korea, most commonly known as LCK. In this research, a method, namely k satisfiability based reverse analysis method (kSATRA) is brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games is used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams in LCK, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games is tested based on root mean square error (RMSE), mean absolute error (MAE) and CPU time. The main issue was to determine the relationship among gameplays or objectives and how they affect the outcome of the game. Results acquired from the computer simulation shows the effectiveness of kSATRA in exhibiting the performance of the LoL teams. [ABSTRACT FROM AUTHOR]

Restricted Boolean 2 Satisfiability (2SAT) is a variant of discrete constraint satisfaction problem, commonly being applied with Hopfield neural network (HNN) as logic programming. In recent years, the binary Ant colony optimization has been formulated to solve various satisfaction and optimization problem due to the power onlooker bee phase in attaining the global convergence. The core motivation of this research is to propose a hybrid Hopfield neural network (HNN) incorporated ACO in enhancing the learning phase of new type of 2SAT logic programming. The developed hybrid model will be compared with the other conventional learning method in hybrid HNNs models such as genetic algorithm (GA) and exhaustive search (ES). The simulations were conducted by training and testing the developed model and the other counterparts with randomized simulated data sets. Therefore, the results manifested the capability of ACO in improving the learning phase of HNN as compared with GA and ES under different complexities. [ABSTRACT FROM AUTHOR]

Hopfield Neural Network (HNN) is a sort of neural network that is strongly dependent to energy minimization of solution. Although HNN managed to solve various optimization problem, the output of HNN suffered from a lack of interpretability and variation. This has severely limited the practical usability of HNN in doing logic programming. Inspired by random neuron perturbation, Estimation of Distribution Algorithm (EDA) has been proposed to explore various optimal neuron state. EDAs employs a probabilistic model to sample the neuron state in order to move toward the various optimal location of global minimum energy. In this paper, a new Mutation Hopfield Neural Network (MHNN) will be proposed to do k Satisfiability programming. Based on the experimental result, the proposed MHNN has outperformed conventional HNN in various performance metric. [ABSTRACT FROM AUTHOR]

Clonal Selection Algorithm (CSA) is a sturdy evolutionary algorithm that mimics the biological immune system mechanism, frequently implemented as a searching technique in solving complex modelling tasks such as pattern recognition and combinatorial optimization. Subsequently, 3-Satisfiability logic mining is the fundamental medium to induce real data sets in a more precise manner. In this paper, a modified clonal selection algorithm with Hopfield neural network (HNN) is proposed for 3-Satisfiability data mining. Somatic hypermutation operator in CSA can reduce the iterations during HNN training phase and retrieve the best logical rule for a minimal testing error. In addition, 3- Satisfiability logical rule provides better attributes representation of the data set. The proposed method will be applied on Amazon's Employees Resources Access data set to predict the approval or denial for an unseen set of employees in the future. The performance evaluation of the proposed method will be compared with exhaustive search (ES) by computing plausible errors such as root mean square error (RMSE), mean absolute error (MAE), sum of squared error (SSE), accuracy and computational time. The computational simulations are carried out by manipulating different number of neurons to verify the capability of the proposed method in data mining. The experimental results have shown a better performance of the proposed model in training and retrieval phase of the Amazon's Employees Resources Access data set in comparison with ES used in this research. [ABSTRACT FROM AUTHOR]

Developing a data mining model for commodities price plays an important role in future investments and decisions for related companies. Viewed from this perspective, this paper proposes a logic mining model for accurately showcase the behavior of the commodities' price from 2009 until 2018. This model utilizes 2 Satisfiability Reverse Analysis Method (2SATRA) integrated with Hopfield Neural Network (HNN). HNN is a single layered neural network that can be divided into learning and retrieval phase. In this case, the retrieved neuron state from HNN is an important component in 2SATRA. The inputs of the proposed model were realized by using the real commodities such as Palm oil, Latex, Gold, Crude Petroleum, Timber, Black & White Pepper and Cocoa Bean. This model discusses the implication of the 2SATRA model within the context of the discipline as well as practical application. [ABSTRACT FROM AUTHOR]

3-Satisfiability Reverse Analysis Method (3SATRA) incorporated with Hopfield neural network is a brand-new approach in extracting the logical rule in the form of 3-Satisfiability (3-SAT) to represent the behavior of a specific medical data set. The motivation of this research is to develop a robust hybrid algorithm to be applied in extracting the information and insights in medical data set. More specifically, 3SATRA is chosen in extracting the insights in term of logical rule from medical data sets. The 3SATRA approach will be combined with 3-SAT logic and HNN as a single data mining paradigm. The proposed method is employed to test and train the medical data set such as Breast Cancer Coimbra and Statlog Heart data set, generated from the standard UCI machine learning repository. The simulation is coded and executed using Dev C++ 5.11 by employing 60% training data and 40% of testing data. The performance of the method was measured based on standard performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), accuracy and CPU Time. The overall results show that the effectiveness of the proposed method in processing the medical data, in terms of lower RMSE, MAE, SSE and faster CPU Time. Additionally, the proposed models have achieved high and consistent accuracy after each of execution. [ABSTRACT FROM AUTHOR]

The random satisfiability (RAN-SAT) is a problem that decides whether there exists a mapping of truth values to variables that makes a given random Boolean formula satisfiable. This paper gives a brief discussion on artificial neural network, Hopfield neural networks and logic programming integration. It proposed a method of optimizing random satisfiability in Hopfield neural networks based on the model energy minimization scheme. Computer simulation is carried out to demonstrate and verify the feasibility to carrying out random satisfiability in the Hopfield network. [ABSTRACT FROM AUTHOR]