Knowledge Representation
Knowledge representation is a field of study within Artificial Intelligence (AI) that aims to encode knowledge in a formal, machine-readable way. It involves developing models, languages, and algorithms for representing and reasoning about knowledge in a computer-interpretable form. It is closely related to the fields of natural language processing, automated reasoning, and automated planning.
Definition
Knowledge representation is the process of taking facts or ideas and encoding them in a way that is understandable for a machine to interpret and act upon. It is a core component of Artificial Intelligence, as it enables computers to draw conclusions from data, interpret it, and make decisions. Knowledge representation is usually achieved through a combination of formal logic and ontologies.
History
Knowledge representation has been an important part of AI research since its inception in the 1950s. Early knowledge representation systems were based on first-order logic and used knowledge representation languages, such as the Situation Calculus and the Frame Problem. Later, these techniques were supplemented by methods such as semantic networks and frames, which were used to represent knowledge in more natural forms.
More recently, knowledge representation has been used to create knowledge bases for use in question-answering and natural language processing systems. These systems utilize ontologies, which are structured sets of knowledge that provide a common understanding of the world. Ontologies are used to represent the domain of knowledge and to support reasoning about it.
Characteristics
Knowledge representation has the following characteristics:
• It is a formalized method of capturing knowledge in a machine-readable form.
• It involves developing models, languages, and algorithms for representing and reasoning about knowledge in a computer-interpretable form.
• It is closely related to the fields of natural language processing, automated reasoning, and automated planning.
• It enables computers to draw conclusions from data, interpret it, and make decisions.
• It is usually achieved through a combination of formal logic and ontologies.
• It is used to create knowledge bases for use in question-answering and natural language processing systems.
References
Albacete, P. L., & Kaminka, G. A. (2012). Autonomous agents: Representation, reasoning and learning. In Autonomous Agents and Multi-Agent Systems (pp. 1-38). Springer, Berlin, Heidelberg.
Dixon, C. (2005). Knowledge representation and reasoning. Morgan Kaufmann.
Luger, G. F., & Stubblefield, W. A. (1993). Artificial intelligence: Structures and strategies for complex problem solving. Addison-Wesley.
Rao, A. S., & Georgeff, M. P. (1995). Modeling rational agents within a BDI-architecture. In Principles of knowledge representation and reasoning: Proceedings of the Second International Conference (pp. 473-484). Morgan Kaufmann.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach. Pearson Education.