In agents that operate in environments where decision-making needs to take into account, not only the environment, but also the minimizing actions of an opponent (as in games), it is fundamental that the agent is endowed with the ability of progressively tracing the profile of its adversaries, in such a manner that this profile aids in the process of selecting appropriate actions. However, it would be unsuitable to construct an agent with a decision-making system based only on the elaboration of such a profile, as this would prevent the agent from having its “own identity,” which would leave the agent at the mercy of its opponent. Following this direction, this study proposes an automatic Checkers player, called ACE-RL-Checkers, equipped with a dynamic decision-making module, which adapts to the profile of the opponent over the course of the game. In such a system, the action selection process is conducted through a composition of multilayer perceptron neural network and case library. In this case, the neural network represents the “identity” of the agent, i.e., it is an already trained static decision-making module. On the other hand, the case library represents the dynamic decision-making module of the agent, which is generated by the Automatic Case Elicitation technique. This technique has a pseudo-random exploratory behavior, which allows the dynamic decision-making of the agent to be directed either by the opponent’s game profile or randomly. In order to avoid a high occurrence of pseudo-random decision-making in the game initial phases—in which the agent counts on very little information about its opponent—this work proposes a new module based on sequential pattern mining for generating a base of experience rules extracted from human expert’s game records. This module will improve the agent’s move selection in the game initial phases. Experiments carried out in tournaments involving ACE-RL-Checkers and other agents correlated to this work, confirm the superiority of the dynamic architecture proposed herein.

ACE-RL-Checkers: decision-making adaptability through integration of automatic case elicitation, reinforcement learning, and sequential pattern mining

The adequate representation of states in the construction of intelligent agents is fundamental for allowing them to achieve a satisfactory performance, principally for those that actuate in a competitive environment that possesses a high state space. One particular type of representation that is very appropriate for these situations is the NetFeatureMap, which describes by means of features the relevant aspects that are inherent to the environment where the agent actuates. In renowned intelligent agents, such features are manually selected, which certainly leads to inadequate choices. In this way, the main contribution of this paper is to propose a new approach, based on Association Rules, that automatically selects these features. Under the intent of investigating the efficacy of such a proposal, the authors utilize the domain of Checkers player agents as their study laboratory. The best performance of the Association Rules-based agents proves the efficacy of the present proposal.

Improving the State Space Representation through Association Rules

Opinion mining also known as sentiment analysis, involves customer satisfactory patterns, sentiments and attitudes toward entities, products, services and their attributes. With the rapid development in the field of Internet, potential customer’s provides a satisfactory level of product/service reviews. The high volume of customer reviews were developed for product/review through taxonomy-aware processing but, it was difficult to identify the best reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is developed to predict the pattern for service provider and to improve customer satisfaction based on the review comments. Associative Regression based Decision Rule Mining performs twosteps for improving the customer satisfactory level. Initially, the Machine Learning Bayes Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After that, Regressive factor of the opinion words and Class labels were checked for Association between the words by using various probabilistic rules. Based on the probabilistic rules, the opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of service preferred by the customers with their review comments. The Associative Regressive Decision Rule helps the service provider to take decision on improving the customer satisfactory level. The experimental results reveal that the Associative Regression Decision Rule Mining (ARDRM) technique improved the performance in terms of true positive rate, Associative Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of similar pattern.

/pdf/associative-regressive-decision-rule-mining-for-predicting-4l9i5jmoj2.pdf

Associative regressive decision rule mining for predicting customer satisfactory patterns

The adequate representation of states in the construction of intelligent agents is fundamental for allowing them to achieve a satisfactory performance, principally for those that actuate in a competitive environment that possesses a high state space. One particular type of representation that is very appropriate for these situations is the NetFeatureMap, which describes by means of features the relevant aspects that are inherent to the environment where the agent actuates. In renowned intelligent agents, such features are manually selected, which certainly leads to inadequate choices. Thus, investigating adequate approaches that perform automatic selection of these features becomes a crucial task. In this way, the main contribution of this paper is to propose a new approach that automatically selects appropriate features based on the frequency at which they occur in the states explored by the agent in the course of its acting over the environment. Such an approach is based on Frequent Pattern Mining. It is interesting to point out that there also exist Genetic Algorithms-based approaches that successfully cope with the same task. Unlike Genetic Algorithms that use heuristic functions to select the features, the present proposal uses real data contained in a specialized database for performing this task. Under the intent of investigating the efficacy of such a proposal, the authors utilize the domain of Checkers player agents as their case study, since they operate in a competitive environment with a very wide state space. This investigation is performed by means of tournaments in which agents whose features are selected by the approach proposed herein face others whose features are selected either manually or by Genetic Algorithms. The superior performance of the Frequent-Pattern-based agents in the tournaments proves the efficacy of the present proposal.

Improving NetFeatureMap-Based Representation through Frequent Pattern Mining in a Specialized Database

This study uses the Arduino Mega board to develop related peripheral modules such as RFID cards and combined with cloud computing to form an ˹RFID Board Game Cloud Computing System˼. This system uses the board game to learn how to construct the computer hardware. Use a simple pairing mechanism. Write the RFID reading information through the program to auto judge whether the student is successfully paired and achieve the purpose of game-based learning. This system can also record the game process of each student and upload it to the cloud server. Through the operation and analysis, the results are provided to the instructions for reference to understand the students' learning processes and problems. At present, the system has completed the construction of system interface and game mechanism, included: RFID card reader interface ‘Arduino main control board program’ game card and system functions can be normal online and operation. In addition to the program function can perform read RFID cards and execute pairing rule, the reading data is displayed on the computer screen to understand the students' learning process.

Study of RFID Board Game Cloud Computing System

In board games, game-logs record past game processes, which can be regarded as an accumulation of experience. Similar to a real person, a computer player can gradually increase its skill by learning from game-logs. Therefore, the game becomes more interesting. This paper proposes an extensible approach to mine experiential patterns from increasing game-logs. The computer player improves its strategies by utilizing these growing patterns, just as it acquires experience. To evaluate the effect and performance of the approach, we designed a sample board game as a test platform and elaborated an experiment consisting of a series of tests. Experimental results show that our approach is effective and efficient.

/pdf/mining-experiential-patterns-from-game-logs-of-board-game-3xzd62bj43.pdf

Mining experiential patterns from game-logs of board game

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Mining experiential patterns from game-logs of board game