The PAL algorithm was tested in a robot combat scenario against both static and learning opponent teams. The Personality Adjusted Learner (PAL) algorithm is applied to heterogeneous teams of robots with reward adjustments modified from earlier work on homogeneous teams and an information-based action personality type assignment algorithm has been incorporated. The learning algorithm assigns strictly local credit assignments to individual agents promoting scalability of the team size. Individual agents on the team have heterogeneous capabilities and responsibilities. This paper presents a reinforcement learning algorithm, which is inspired by human team dynamics, for autonomous robotic multi agent applications. This was accomplished by each agent learning to pick specific battle strategies for each different opponent it faced. This system was evaluated in a simulated melee combat scenario and was shown to learn to improve its performance over time. In addition, the problem of delayed reward is addressed through a time-averaged reward applied to several sequential actions at once. Each agent controls a different aspect of the robot’s behavior. The agents learn through reinforcement learning, and are loosely coupled by their reward functions. This paper presents a novel multi-agent system that cooperates to control a single robot battle tank in a melee battle scenario, with no prior knowledge of its opponents’ strategies. A common approach is to use reinforcement learning to allow an agent controlling the robot to learn and adapt its behavior based on a reward function. Os resultados apontam que a integração das disciplinas em um ambiente de jogo, devidamente planejado, pode apoiar o ensino e aprendizagem dos conteúdos matemáticos e computacionais de modo lúdico.Īs robotic systems become more prevalent, it is highly desirable for them to be able to operate in highly dynamic environments. Os conteúdos pertinentes à matemática estão relacionados ao ensino e aprendizagem do plano cartesiano e são integrados à linguagem de programação com conceitos introdutórios na orientação à objetos. Os desafios foram construídos a partir das abordagens do Pensamento Computacional e apresentam aportes entre conceitos matemáticos e de programação de computadores para determinar estratégias de comportamento e a programação dos robôs dentro do jogo. A presente proposta apresenta desafios no ambiente do jogo educacional com simulador de robôs, Robocode.
Planejar ambientes com jogos digitais podem ser uma estratégia lúdica para ensino de matemática e programação de computadores.
#Prolog adventure game source code software#
The purpose of the study is to develop and implement a software module using fuzzy logical inference in a medical information system.KeywordsFuzzy logicSWI-PrologKnowledge baseExpert systemsĮstudantes das novas gerações, imersos num contexto digital, podem demonstrar desmotivações quando inseridos somente em ambientes considerados como tradicionais para ensino de disciplinas como matemática. To test the knowledge base, a graphical interface of the system was developed using the XPCE cross-platform library, which is included in the SWI-Prolog software environment. The paper outlines the main stages of the life cycle of creating a system using fuzzy logic, which include all the key stages of system design.
The knowledge base of the expert system was developed using the SWI-Prolog software environment, which supports the necessary software libraries that provide the construction of the graphical shell of the expert system, as well as dynamic processing of fuzzy rules.
The Django framework was used to develop the client part, and the PySwip module was used to process knowledge bases. Python and Prolog programming languages were used to develop the client-server application. We strive to contribute the results of our research to the development of medical software products, namely, to increase the efficiency of medical services using artificial intelligence. To develop a medical expert system, we considered scales and algorithms for assessing the prognosis of the severity of community-acquired pneumonia PORT (PSI), CURB/CRB-65 and SMART-COP/SMART-CO. Human health depends on making the right decision, as it can be difficult for a doctor to choose the correct diagnosis and treatment of pneumonia. The use of fuzzy logic is an urgent direction in cases of incomplete certainty when making a medical diagnosis. The paper describes the process of building a fuzzy expert system for assessing the severity of pneumonia.
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