Photo de Diego González sur UnsplashSource : Thales

Challenge

AI x Mission Planning & Evaluation

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Challenge Overview

Description du challenge

In the context of mission planning and evaluation, it is crucial to ensure that missions are meticulously planned and their outcomes accurately evaluated. This process is essential in various fields, including defense, space exploration, and complex industrial operations. Two critical aspects must be addressed to achieve this:

The first aspect involves establishing a reward function that accurately reflects the needs and objectives of operators and users. These needs can vary widely based on the type of users, their specific goals, and the mission context. Accurately defining this reward functions is essential to align mission objectives with desired outcomes, ensuring that the mission planning process is tailored to meet the unique requirements of each mission scenario. This alignment enables more effective decision-making and enhances the overall success of the mission.

The second aspect, which is closely linked to the reward function, involves analyzing the behavior of an AI agent and comparing it to the behavior of a human operator. This comparison is crucial for assessing the performance of the AI agent and ensuring that it can effectively mimic or enhance human decision-making processes. Understanding how the AI agent behaves in various scenarios, and how its actions align with human operators' expectations and actions, is key to refining the AI's performance and ensuring that it can reliably support or augment human operators in mission-critical tasks.

Photo de Chris Liverani sur Unsplash
Photo de Chris Liverani sur Unsplash
Photo de Markus Spiske sur Unsplash
Photo de Markus Spiske sur Unsplash

Wanted

Expert in:
• Reinforcement learning
• Behavioral analysis
• Human-computer interaction
• Data science


Experience in:
• Reward function design
• Agent-based modeling
• Performance evaluation

Projects you could be working on

Developing AI algorithms to establish dynamic reward functions based on varying user needs.

• Analyzing and comparing agent behaviors with human behaviors to evaluate and enhance agent performance.

• Creating simulation environments to test and refine mission planning strategies.

• Designing tools for operators to define and adjust reward functions in real-time.