Artificial intelligence is the most prominent blessing of the allied disciplines of neuroscience and computer sciences. Like a human being, an artificially intelligent entity must have some knowledge to act and get trained. This process of acquiring knowledge and acting based on the experiences gained by knowledge in the case of machines is known as knowledge representation. The data we generate every day by a plethora of processes. This data is analyzed for the generation of knowledge and then this knowledge is used to train and calibrate artificially intelligent agents. The representation of knowledge for the training of AI entities must follow a format or a standardized paradigm. The validation and structuring of this knowledge are essential for the correctness of the training and ultimately the performance of the AI. This article will concentrate on this very important process. And discuss different aspects of knowledge representation in AI.
Knowledge and intelligence
In the case of human beings knowledge is the sole source of analytical and critical thinking. Without knowledge, we humans can not analyze and behave in accordance with the situation. Just like us machines, attributed with artificial intelligence must have the necessary amount of information and knowledge in order to act upon a problem or situation. Arithmetic, adaptive behavior, generativity, abstract reasoning, working memory and adaptive behavior are the most essential subsets of intelligence and only with the knowledge these subsets can develop and can emerge as useful traits. Thus the origin of intelligence is in knowledge and the good practice of harnessing knowledge in the right manner.
Different kinds of knowledge representation in AI
To achieve a desired goal or result from an artificially intelligent entity, it is important to train the same with the most relevant and right kind of knowledge. Thus presenting knowledge in a manner that the machine might understand is of utmost importance. The domain of knowledge can be classified into the following classes.
- Structural knowledge
Structural knowledge is the knowledge of relationships between an object and the concept, driving a particular process.
- Declarative knowledge
Declarative knowledge is facts and truths, might also involve processes and objects as knowledge.
- Heuristic knowledge
Heuristic knowledge represents the rules of thumb and the extracts of previous research.
- Metaknowledge
Metaknowledge is the knowledge of knowledge itself. Meta studies are done in order to connect different endeavours of generating knowledge and derive a greater and zoomed out perspective of something.
- Procedural knowledge
Procedural knowledge is mostly technical knowledge concerning processes and protocols.
Knowledge and intelligence
In the case of human beings knowledge is the sole source of analytical and critical thinking. Without knowledge, we humans can not analyze and behave in accordance with the situation. Just like us machines, attributed with artificial intelligence must have the necessary amount of information and knowledge in order to act upon a problem or situation. Arithmetic, adaptive behavior, generativity, abstract reasoning, working memory and adaptive behavior are the most essential subsets of intelligence and only with the knowledge these subsets can develop and can emerge as useful traits. Thus the origin of intelligence is in knowledge and the good practice of harnessing knowledge in the right manner.
How knowledge is utilized by AI agents?
AI systems utilize knowledge following a standard method in order to get the training and act properly upon particular scenarios. This multi staged process can be classified as follows.
- Perception
Perception of knowledge is handled by perception components of an AI. in order to register this knowledge, the knowledge must be presented in a perceivable form so that the machine can understand what’s on offer. Knowledge can be received in the form of visual, audio or even texts. And in order for the AI system to record all these relevant receptors are needed.
- Learning
Familiarization with the perceived information and making it ready for the next stages is the learning phase. In this stage, an AI system learns about different aspects of the knowledge like its origin, temporal aspects and structural characteristics.
- Representation and reasoning
These stages are concerned with training the AI with the help of properly structured information and knowledge. Representation of knowledge should be welcomed for the machine so that proper reasoning can be conducted.
- Planning and execution
Planning a process is solely dependent on how the knowledge is represented and what reasoning the AI system could perform. Thus these stages are dependent on the correctness of the previous perception and learning stage. Planning and execution stages are thus intricately intertwined and mutually independent at the same time.
