Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two dimensions. In soccer, passing behavior is an essential action for keeping the ball in possession of our team and creating goal opportunities. Similarly, for SS2D, predicting the passing behaviors of both opponents and our teammates helps manage resources and score more goals. Therefore, in this research, we have tried to address the modeling of passing behavior of soccer 2D players using Deep Neural Networks (DNN) and Random Forest (RF). We propose an embedded data extraction module that can record the decision-making of agents in an online format. Afterward, we apply four data sorting techniques for training data preparation. After, we evaluate the trained models' performance playing against 6 top teams of RoboCup 2019 that have distinctive playing strategies. Finally, we examine the importance of different feature groups on the prediction of a passing strategy. All results in each step of this work prove our suggested methodology's effectiveness and improve the performance of the pass prediction in Soccer Simulation 2D games ranging from 5% (e.g., playing against the same team) to 10% (e.g., playing against Robocup top teams).

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The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate's passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents' players.

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The last decade has witnessed the rise of a black box society where classification models that hide the logic of their internal decision processes are widely adopted due to their high accuracy. In this paper, we propose FEHAN, a modularized Framework for Explaining HiErarchical Attention Network trained to classify text data. Given a document, FEHAN extracts sentences most relevant to the assigned class. It then generates a set of similar sentences using a Markov chain text generator, and it replaces the salient sentences with the synthetic ones, resulting in a new set of semantically similar documents in the vicinity of a given instance. The generated documents are used to train an interpretable decision tree that identifies words explaining the reason for the classification outcome. A quick inspection of these synthetic documents and their salient words helps explain why the black-box has assigned a given class to a document. We performed a qualitative and quantitative evaluation of FEHAN and a baseline on four different datasets to show the effectiveness of our proposal.