335 – Research Assistant, Explainable AI for Agent-based Simulation

Project Name
Explainable AI for Agent-based Simulation

Project Description
In team-based Synthetic Training Environments (STEs), populated with AI-driven entities, the reasoning process behind these AI entities offers great opportunities for teams to understand what happened during training, why it happened, and how to improve. Unfortunately, while there are existing agents that can generate realistic actions in simulated exercises, they typically cannot describe events from their perspective or explain the reasoning behind their behaviors. This is often due to the fact that the algorithms underlying AI-driven entities are not readily explainable, making the resulting behavior hard to understand even for AI experts. This project addresses this challenge by incorporating explainable artificial intelligence (XAI) to support explainable agent behaviors. Although specific methods vary, depending on the targeted AI algorithms, the XAI interface creates an interpretable model for the underlying algorithms. Components of the interpretable models can then be used to create explanations of the decision-making of the AI entities.

Job Description
The research assistant will work with existing agent frameworks and machine learning algorithms to develop explainable models for the AI algorithms.

Preferred Skills
– Good knowledge of AI algorithms
– Python, C/C++
– Good knowledge of math

Apply now.

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