Real-Time Strategy Games Bot Based on a Non- Simultaneous Human-Like Movement Characteristic

This paper discusses how to improve the behaviour ofartificial intelligence (AI) algorithms during real-time strategygames so as to behave more like human players. If we want toachieve this goal we must take into consideration several aspectsof human psychology – human characteristics. Here we focusedon the limited reaction times of the players in contrast to theenormous speed of modern computers. We propose an approachthat mimics the limitations of the human reaction times. In orderto work properly, the AI must know the average reaction times ofthe players. Some techniques and proposed algorithm outline arepresented on how to achieve this

Universal Algorithm for Creating A Small Scale Reusable Simulation Data in Real-time Strategy Games

Real-time strategy games are of such high complexity that consideration of trying to brute force all actions and states is not only impractical, but impossible. Approximations, information abstractions, and models are, therefore, the necessity when creating game bots that play this genre of games. To create such bots, the detailed data is needed to base them on. This article introduces a universal algorithm that creates reusable simulation data of one attacking unit on a building and tests the feasibility of doing such a task. This paper concludes that capturing all relevant data in a sub-segment of real-time strategygames is feasible. Gathered data holds valuable information and can be reused in new research without the need of repeating the simulations

An Experiment in Design and Analysis of Real-Time Applications

In the paper some experiences of joining two methodologies, which were originally independently developed in different institutions, with the goal to overcome the possible discrepancies due to the separate design of the hardware and the software part of an embedded real-time application are presented. Based on Multiprocessor PEARL, Specification PEARL has been developed in FERI, Maribor. Hardware and system architecture of an application can be described and gradually refined. Application software can be designed using LACATRE tool, developed at INSA, Lyon. Decisions about the application design taken in each tool have influence to the ones taken in the other, thus allowing for parallel design of both parts. Both designs are subsequently verified and eventually joined for feasibility estimation by co-simulation. The application program is coded using the ObjectPEARL language. The real-time system design cycle is closed by the execution time analysis and measurements upon which it is then considered about further program and/or hardware part reconfiguration. This feature is supported by the specific compiler, which includes the execution time analyser. The article reports on the work that was done in the framework of the PROTEUS project in co-operation of the teams from FERI Maribor, Slovenia, and INSA de Lyon, France

Accuracy is not enough: optimizing for a fault detection delay

This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so

Applying LCS/XCS to the RTS Games Domain

Real-Time Strategy games (RTS) are representatives of the highest class of computational complexity in computer game genres. To cope with the high complexity of the state-action space of RTS game worlds, various Machine Learning algorithms are being used and researched extensively. In this article, we apply eXtended Classifier Systems (XCS) to the domain of RTS games. The XCS algorithm belongs to a Learning Classifier Systems (LCS) group known for their adaptability, generalisation, and scalability. We build the game agent named AIXCS. It uses a group of XCS algorithms, which generate a set of unit-actions used in the RTS game. The AIXCS operates without prior learning from the game runs and in tight timing constraints. The AIXCS was put to the test against other game agents in the micro RTS game environment, with positive results regarding successful game operation at runtime