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Artificial intelligences methods for discovering solutions to problems can be implemented with and without understanding the domain, depending on the circumstance. AI decision-making has been studied from various functional and industrial viewpoints in academic and practical literature. As AI-based services continue to advance, more personal and important decisions are being left to the technology. However, the two fundamental principles of propositional and first-order logic are the foundation for all AI-based technologies.
The first core element is propositional logic, which uses Boolean reasoning to transform real-world data into a machine-readable structure. Such reasoning applies to knowledge-based expert systems or AI-based systems that make decisions or judgments similar to those of experts. Completeness, consistency, and tractability may be required to very effectively and efficiently formulate the domain knowledge as theory (Neapolitan & Jiang, 2018). Complex prepositions link one, two, or more sentences together. In propositional logic, the syntax used to indicate the joining of two or more sentences is created using symbols. An appropriate structure for presenting information is known as syntax.
Artificial intelligences propositional logic analyzes sentences as variables, and in the event of complicated sentences, the first phase is to deconstruct the sentence into its component variables. The stages that carry out the intended task are hard-coded in the script when a system is designed procedurally (Kakas et al., 2017). The knowledge and reasoning are divided into the declarative method. This strategy helps develop wumpus algorithms that reason with knowledge bases and when using forward and backward chaining to reason with rules. Hence, propositional logic in artificial intelligence is essential to realize the promise of machine learning and decision-making fully.
Another method of information processing in artificial intelligence is first-order logic. To concisely convey the natural language statements is an extension of propositional logic. A machine that uses a comprehensive knowledge base, such as First Order Logic, might be able to reason about a wide range of global issues. It could plan appropriately, reason from first concepts, argue about its objectives, and explain how its activities in the world interact with one another.
These computers can be seen performing these tasks in research centres and laboratories. In order to effectively account for the ambiguity or imprecision of the natural world, Garrido (2010) claims that the idea of sets, relations, and other ideas must be transformed by including logical concepts and procedures. Additionally, the potential for the abstraction of propositional logic is constrained because it does not permit the conduct of reasoning over variables and functions with generic and dynamic content. It implies that the early logical computing systems were likewise incapable of resolving issues whose solutions are included within the vector spaces whose subspaces the propositional space belongs to. First-order logic, a formal logical system that incorporates variables and enables abstraction, as a result, helped to solve this issue.
As a result, decision-making results in behaviors depending on the information and comprehension that the agent has been given. A portion of the risk associated with a decision is transmitted to the inputs of the decision-making agent if it directly affects the environment. In the area of artificial intelligence, the connection has received extensive research. Studies already conducted generally focus on constructing an AIs reasoning frameworks using classical logic, or at least parts of it. However, first-order and propositional logic provides the fundamental knowledge for the continued development of AI systems.
References
Garrido, A. (2010). Logical foundations of artificial intelligence. Brain: Broad Research in Artificial Intelligence and Neuroscience, 1(2), 149-152.
Kakas, A. C., Mancarella, P., & Toni, F. (2017). On argumentation logic and propositional logic. Studia Logica, 106(2), 237279. Web.
Neapolitan, R. E. & Jiang, X. (2018). Artificial intelligence with an introduction to machine learning. CRC Press.
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