Artificial Intelligence-Guided Machine Learning Frameworks for Zero-Shot Decision Making in Autonomous Systems

Abstract

There has never been a greater need in the changing world of artificial intelligence (AI) and autonomous systems to have intelligent agents that can be deployed in the new and unpredictable environment. Traditional machine learning (ML) models can also make use of task specific training data, making the models less able to learn general able to make inferences not described or encountered in previously seen cases and scenarios. In the current paper, the author offers an AI-powered machine learning architecture of zero-shot decision making found that will deploy the technologies of autonomous agents such as drones, robots, and driverless cars. This proposed hybrid system is an integration between the symbolic reasoning AI and representation models trained using deep learning so that the agents can interpret new inputs and provide decisions based on the context but they have never been exposed to that kind of environment.

We begin by evaluating existing efforts in zero-shot learning (ZSL) and realize grave inadequacies with regards to them being adapted to real-time decision making in dynamic settings. In addition to that, we construct a hybrid architecture, which is a combination of contrastive vision-language pertaining (e.g., CLIP) and neuron symbolic reasoning blocks enabling improved generalization. The criterion according to which the model will be tested is a range of simulated conditions, performance measures are precision, the overall percentage of success, and time spent to complete a task. The effectiveness of the model and its relative superiority to state-of-the-art ZSL models have been illustrated with the help of such visual aids as bar charts, pie charts, architectural diagrams.

Empirical results in our model result in a considerable gain with respect to zero-shot decision-making with an average of 87.6 percent in generalization accuracy on tasks that the model has never encountered. Researchers can suggest that the difficulties that narrow AI, or rather general, autonomy faces, can be occupied by hybrid AI- ML systems as the results indicate. The study can also be applied in early developments of strong, flexible, and smart autonomous systems that can be used in realistic environments such as the disaster zones, military surveillance and deep-space exploration environment specifically in high-risk and inaccessible areas.