Hybrid Intelligence introduces Explainable Reinforcement Learning (XRL), where agents not only maximise rewards but also justify their actions through an Explanatory Space, enabling machine introspection. Learn how this enhances trust, transparency, and decision-making in high-stakes applications.

Understand the dual functionality of information retrieval in Explainable Reinforcement Learning: as a broader knowledge-support mechanism and as a targeted, real-time utility for single agentic decisions. See how this compares both conceptually and functionally with classical retrieval.

Learn how the explanation models in Hybrid Intelligence allow XRL (eXplainable Reinforcement Learning) Agents to introspect by reasoning and “thinking through” the explanation behind a suggested plan of action, before executing the plan – resulting in AI learning mechanism informed by knowledge that is richer in both depth and breadth.

Good explanations are key to building a coherent understanding of reality. Hybrid Intelligence is more than predictive AI, it embeds causal, interpretable structures into its reasoning, providing explanations that are transparent and scientifically robust.

AI seeks to discover knowledge through statistical relationships within data. Hybrid Intelligence’s neuro-symbolic architecture leverages both the statistical pattern-matching power of neural networks together with the analytical reasoning power of symbolic logic systems.