Knowledge Reasoning is a field in Artificial Intelligence (AI) exploring logical reasoning mechanisms to derive insights from stored information or knowledge. It is instrumental in decision-making, problem-solving and understanding complex domains.
Imagine if you’re playing a treasure hunt game. You have a map (knowledge) and clues (information). The process of using the map and clues to figure out where the treasure is - that’s Knowledge Reasoning. It’s about using information you have to figure things out.
Knowledge Reasoning, an AI concept, embodies using data, facts, or information about an area of interest and inferring new pieces of knowledge using logical processes. This is similar to how humans process information and reason to make decisions or generate insights.
In AI, the area of interest could range from a simplified digital environment to complex real-world domains. Each instance of data or ‘knowledge’ could represent facts or rules about this area. Beginning with this knowledge, the AI utilizes reasoning algorithms to derive new knowledge. This often means deducing conclusions that are not explicitly stated in the initial knowledge.
In a programming context, knowledge is often represented as ‘facts’ in a database, and the reasoning process occurs through applying rules that morph these facts into new insights. These reasoning rules could be implicit or explicit and can follow a multitude of reasoning methods such as deduction, induction, or abduction—depending on the scope of the problem defined.
For example, consider an AI trained to reason about weather conditions. Its initial knowledge base might include facts about humidity levels, temperature, and cloud coverage. If the AI knows that high humidity and low temperature often results in foggy weather, it can reason that if the initial knowledge includes high humidity and low temperatures, it is likely to be foggy.
Knowledge reasoning is critical in AI’s ability to understand complex domains that require an understanding of a vast range of facts and their inter-relationships—areas like medical diagnostics or advanced robotics. Importantly, a significant challenge in knowledge reasoning is ensuring the robustness and accuracy of the reasoning process since erroneous conclusions can lead to incorrect decision-making.