This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.

In the context of real-time strategy video games like StarCraft II, strategic decision-making is a complex challenge that requires adaptability and precision. This research creates a mixed recommendation system that uses causal models and deep neural networks to improve its ability to suggest the best strategies based on the resources and conditions of the game. PySC2 and the official StarCraft II API collected data from 100 controlled matches, standardizing conditions with the Terran race. We created fake data using a Conditional Tabular Generative Adversarial Network to address data scarcity situations. These data were checked for accuracy using Kolmogorov–Smirnov tests and correlation analysis. The causal model, implemented with PyMC, captured key causal relationships between variables such as resources, military units, and strategies. These predictions were integrated as additional features into a deep neural network trained with PyTorch. The results show that the hybrid system is 1.1% more accurate and has a higher F1 score than a pure neural network. It also changes its suggestions based on the resources it has access to. However, certain limitations were identified, such as a bias toward offensive strategies in the original data. This approach highlights the potential of combining causal knowledge with machine learning for recommendation systems in dynamic environments.

Antecedentes: Son varias las técnicas que permiten desarrollar modelos de comportamiento artificial poco predictivo, como por ejemplo, las máquinas de estados finitos (FSM) y el uso de arquitecturas cognitivas basadas en la teoría de la mente, para la construcción de agentes cuyo modelo conductual reside sobre un sistema de producciones. Objetivo: Se propuso generar modelos conductuales artificiales para determinar las condiciones en que estos demuestran comportamientos poco predictivos. Metodología: La primera etapa consistió en la elección de plataformas y herramientas. Se escogieron Pogamut, UT2000, SOAR y Java; en la segunda etapa se desarrolló la interfaz de acoplamiento entre el motor de cognición, el lenguaje y el entorno, por último, en la tercera etapa, se efectuaron pruebas con los modelos de comportamiento. Resultados: En el modelo FSM fue posible contrastar los estados y las decisiones que toman los agentes cuando se presentan restricciones en el conjunto de acciones predefinidas en su lógica. Así mismo fue posible el contraste entre producciones SOAR en cuanto a la predictibilidad de las acciones del agente a razón de lo percibido en el entorno. Conclusiones: Las máquinas de estados finitos son un componente importante cuando se quiere inspeccionar el comportamiento reactivo de un agente que persigue un único objetivo. Los agentes reflejos dependen de su lógica para la percepción inmediata de su entorno sin tener en cuenta las decisiones que han tomado o estados por los que ya hayan pasado. Los programas SOAR ajustan la retroalimentación de sus ambientes en ciertos casos.