This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive.

When it comes to game playing, evolutionary and tree-based approaches are the most popular approximate methods for decision making in the artificial intelligence field of game research. The evolutionary domain therefore draws its inspiration for the design of approximate methods from nature, while the tree-based domain builds an approximate representation of the world in a tree-like structure, and then a search is conducted to find the optimal path inside that tree. In this paper, we propose a novel metric for game feature validation in Real-Time Strategy (RTS) games. Firstly, the identification and grouping of Real-Time Strategy game features is carried out, and, secondly, groups are included into weighted classes with regard to their correlation and importance. A novel metric is based on the groups, weighted classes, and how many times the playtesting agent invalidated the game feature in a given game feature scenario. The metric is used in a series of experiments involving recent state-of-the-art evolutionary and tree-based playtesting agents. The experiments revealed that there was no major difference between evolutionary-based and tree-based playtesting agents.

This article is positioned into a cross-section of machine learning, cybersecurity, and nature-inspired domains. This article’s main objective is to use eXtended Classifier System (XCS), a known adaptive Reinforcement Learning (RL) algorithm, and alter it to use the Bacterial Foraging Optimization Algorithm (BFOA) instead of its original Genetic Algorithm component. This modification transforms XCS into a multi-criteria optimization system (BFOA-XCS) through evaluation of classifier fitness across accuracy, stability, and variance reduction while simultaneously using weighted-sum scalarization. In this way, the method leverages BFOA’s chemotactic search and population dynamics. The proposed BFOA-XCS integration was validated in two experimental phases. First, evaluations across 19 benchmark machine learning datasets demonstrated that Improved BFOA (IBFOA)-XCS achieves the best Friedman ranking among all XCS variants (marginally significant at α = 0.10, supported by medium-to-large effect sizes), with notable variance reduction (15.2 percent) over standard GA-XCS. Second, in a dynamic cybersecurity simulation environment with six attack scenarios, all XCS variants significantly outperformed three of five deep RL baselines (Deep Q-Network (DQN), Q-Learning, and Policy Gradient (REINFORCE)) with large statistical effect sizes. Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC) achieved higher overall rewards but at substantially greater computational cost: PPO at 5.3× and SAC at 26.1× the XCS compute time per run (2 min 8 s and 10 min 26 s, respectively, vs. 24 s for XCS). The results demonstrate that rule-based XCS with BFOA optimization offers a compelling alternative to neural approaches for cybersecurity defense, combining competitive performance with interpretable policies and substantially lower computational requirements.

V prispevku predstavljamo razvoj simulacijskega okolja za preučevanje kibernetskih napadov in obrambnih strategij z uporabo inteligentnih agentov, podprtih z velikimi jezikovnimi modeli. Okolje omogoča realistično simulacijo napadov, kot je spletno ribarjenje, kjer napadalni agenti generirajo prepričljiva sporočila, obrambni agenti pa jih analizirajo in zaznavajo znake prevare. Takšna interakcija omogoča varno testiranje v nadzorovanem okolju ter prispeva k razumevanju učinkovitosti obrambnih mehanizmov, izboljšanju algoritmov za zaznavanje prevar in izobraževanju uporabnikov o sodobnih kibernetskih grožnjah.

Games can be more than just a form of entertainment. Game spaces can be used to test different research ideas quickly, simulate real-life environments, develop non-playable characters (game agents) that interact alongside human players and much more. Game agents are becoming increasingly sophisticated as the collaboration between game agents and humans only continues to grow, and there is an increasing need to better understand game players’ workings. Therefore, this work addresses the digital characterization (DC) of various game players based on the game feature values found in a game space, and based on the actions gathered from player interactions with the game space. High-confidence actions are extracted from rules created with association rule mining, utilizing advanced evolutionary algorithms (e.g., differential evolution) on the dataset of feature values. These high-confidence actions are used in the characterization process, resulting in the DC description of each player. The main research agenda of this study is to determine whether DCs manage to capture the essence of players’ action style behavior. Experiments reveal that characterizations do indeed capture behavior nuances, and consequently open up many research possibilities in the domains of player modeling, analyzing the behavior of different players and automatic policy creation, which can possibly be used for utilization in future simulations.