Evaluation and Reliability Aspects of Multi-Modal Reasoning and Decision-Making in Autonomous AI Systems

Abstract

The study measures the performance of multi-modal AI systems in autonomous decision-making; the models are CNN, Hybrid Fusion, and LSTM models. It pays attention to important metrics, such as accuracy, adaptability, and in dynamic conditions, its performance plots, confusion matrices and sensitivity analyses. It has been seen that CNN models are more efficient in terms of accuracy, whereas Hybrid Fusion and LSTM models give mixed results, yet they need to be improved when it comes to noise handling and confidence. This paper highlights the important aspect of increased multi-mode integration to ensure more accurate and meaningful decision-making.