Today’s challenge
Challenges lie in developing sophisticated AI agents that can make autonomous decisions with human-level reliability, while maintaining transparency and accountability. Today’s AI systems make critical decisions in complex, high stake environments in a fast-moving automated world.
These systems often lack transparency, reliability and the ability to handle uncertainty effectively. This poses significant risks in sectors such as healthcare, finance, logistics, and manufacturing where incorrect decisions can have severe consequences.
Our solution
Our research focuses on creating a team of AI agents that combine advanced machine learning techniques with formal verification methods, game theory and explainable AI. By integrating these approaches, we develop AI agents that not only make accurate decisions, but also continuously learn and provide clear and transparent reasoning of their choices.
Our solution incorporates uncertainty quantification and robust decision-making algorithms, ensuring that the AI agents can handle unexpected scenarios and edge cases. This has the potential to revolutionize critical decision-making processes across industries.
1. Perceive
Understand the environment
2. Collaborate
Plan, team building and assign tasks
3. Explore
Logical understanding and uncertainty estimation
4. Learn
Cause and effect relationship learning and adapting
5. Act
Make decisions and execute
Key technology enablers:
Uncertainty estimation
Incorporating probabilistic methods into deep learning models for more robust decision-making under uncertainty.
Formal verification techniques
Applying mathematical proofs to ensure the correctness of AI decision processes.
Explainable AI (XAI) methods
Developing interpretable models that provide human-understandable explanations for their decisions.
Multi-agent reinforcement learning
Enabling collaborative decision-making among multiple AI agents in complex environments.
Causal inference models
Integrating causal reasoning to improve the AI's understanding of cause-and-effect relationships.
Adaptive learning algorithms
Implementing continuous learning capabilities to refine decision-making based on new data and experiences.
Generative AI
Leveraging generative models to create synthetic data and enhance decision-making capabilities.
Informed machine learning
Incorporating domain knowledge and expert insights to guide and improve the learning process.
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