Global optimization of expensive, potentially gradient-free functions has long been a critical component of many complex problems in science and engineering.

Our most powerful artificial agents cannot be told exactly what to do, especially in complex planning environments. They almost exclusively rely on neural networks to perform their tasks, but neural networks cannot easily be told to obey certain rules or adhere to existing background knowledge. While such uncontrolled behaviour might be nothing more than a simple annoyance next time you ask an LLM to generate a schedule for reaching a deadline in two days and it starts to hallucinate that days have 48 hours instead of 24, it can be much more impactful when that same LLM is controlling an agent responsible for navigating a warehouse filled with TNT and it decides to go just a little too close to the storage compartments. Luckily, controlling neural networks has gained a lot of attention over the last years through the development of . Neurosymbolic AI, or NeSy for short, aims to combine the learning abilities of neural networks with the guarantees that symbolic methods based on automated mathematical reasoning offer.

Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

Utilizing mutual learning can effectively enhance the performance of peer BNNs.

Machine learning (ML) models have been widely used to make predictions.

Modularity is a critical design principle for both software systems and artificial intelligence (AI).

Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.