In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications.

The sudden irruption of AI into the world of Law calls for a deep reflection on the education and training of lawyers, and specially, on ethics. This Article offers a new model of AI-driven immersive simulations for the instruction of legal ethics. Leveraging machine learning and natural language processing, the platform generates adaptive, dynamic simulations that emulate complex ethical situations – like conflict of interest, algorithmic bias and client confidentiality in an AI-imbued legal world. Such simulations yield timely feedback which promotes moral-ethical reflection and professional identity. The paper presents the possibilities of using it while discussing the evaluation of its applicability and challenges, which involve scalability, usability and ethical use. The initiative is to give law students and lawyers ethical fluency of AI-intentionalized practice of law and so that the practice can became equitable (read, ethical) for better world by using interdisciplinary collaboration.