Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Modern unmanned systems require the capability to make intelligent decisions in the face of uncertain information. Existing logical frameworks often fall short in addressing the inherent vagueness and randomness present in the available data. In this paper, we introduce an innovative reasoning system called Earthling Logic (EL), developed by emulating the mechanism of human reasoning. EL extends classical propositional and predicate logic to accommodate statements that exhibit varying degrees of vagueness and randomness. Within the framework of EL, we establish statemental algebras and truth measures. We introduce the notions of syntactical consequences and semantical consequences. Building on these ideas, we develop a general deduction principle that rigorously manages uncertainty when deriving statements from premises. We are able to establish the soundness and completeness of this deduction method. Additionally, we provide a general framework for decision-making based on EL, which can enhance the reasoning abilities of intelligent systems. IntroductionModern intelligent systems must possess the ability to make decisions in complex environments [14,16,18]. To endow unmanned systems with this capability, we look to the decision-making process of humans. It's widely recognized that humans typically base decisions on patterns of situations rather than on raw information. Human cognition often operates within these patterns, which are frequently characterized by a degree of vagueness. These patterns are expressed through language, allowing thoughts to be articulated in vague statements. Humans often rely on patterns rather than raw data when making decisions for several reasons rooted in cognitive processes and efficiency:Cognitive Economy: Dealing with raw information, especially in its detailed and unprocessed form, can be cognitively demanding. Patterns allow the brain to simplify complex information and make decisions more efficiently.Information Overload: The amount of information available to humans is vast, and processing all of it in real-time would be overwhelming. By relying on patterns, individuals can quickly filter a nd assess relevant information, making decision-making more manageable.Experience and Learning: Humans accumulate knowledge and experience over time. Recognizing patterns enables individuals to draw upon past experiences and apply learned strategies to current situations. This is a more adaptive approach than starting from scratch with every decision.Heuristics and Mental Shortcuts: Patterns often lead to the development of heuristics or mental shortcuts. These shortcuts help in making rapid decisions based on previous learning and common associations without extensive deliberation.Efficiency an d Ad aptability: Pa tterns pr ovide a fr amework fo r effi cient prob lem-solving. When faced with a situation, humans can quickly identify similarities to past experiences, making it easier to generate solutions. Patterns allow for a degree of adaptability. They are not rigid and can be adjusted b...
Modern unmanned systems require the capability to make intelligent decisions in the face of uncertain information. Existing logical frameworks often fall short in addressing the inherent vagueness and randomness present in the available data. In this paper, we introduce an innovative reasoning system called Earthling Logic (EL), developed by emulating the mechanism of human reasoning. EL extends classical propositional and predicate logic to accommodate statements that exhibit varying degrees of vagueness and randomness. Within the framework of EL, we establish statemental algebras and truth measures. We introduce the notions of syntactical consequences and semantical consequences. Building on these ideas, we develop a general deduction principle that rigorously manages uncertainty when deriving statements from premises. We are able to establish the soundness and completeness of this deduction method. Additionally, we provide a general framework for decision-making based on EL, which can enhance the reasoning abilities of intelligent systems. IntroductionModern intelligent systems must possess the ability to make decisions in complex environments [14,16,18]. To endow unmanned systems with this capability, we look to the decision-making process of humans. It's widely recognized that humans typically base decisions on patterns of situations rather than on raw information. Human cognition often operates within these patterns, which are frequently characterized by a degree of vagueness. These patterns are expressed through language, allowing thoughts to be articulated in vague statements. Humans often rely on patterns rather than raw data when making decisions for several reasons rooted in cognitive processes and efficiency:Cognitive Economy: Dealing with raw information, especially in its detailed and unprocessed form, can be cognitively demanding. Patterns allow the brain to simplify complex information and make decisions more efficiently.Information Overload: The amount of information available to humans is vast, and processing all of it in real-time would be overwhelming. By relying on patterns, individuals can quickly filter a nd assess relevant information, making decision-making more manageable.Experience and Learning: Humans accumulate knowledge and experience over time. Recognizing patterns enables individuals to draw upon past experiences and apply learned strategies to current situations. This is a more adaptive approach than starting from scratch with every decision.Heuristics and Mental Shortcuts: Patterns often lead to the development of heuristics or mental shortcuts. These shortcuts help in making rapid decisions based on previous learning and common associations without extensive deliberation.Efficiency an d Ad aptability: Pa tterns pr ovide a fr amework fo r effi cient prob lem-solving. When faced with a situation, humans can quickly identify similarities to past experiences, making it easier to generate solutions. Patterns allow for a degree of adaptability. They are not rigid and can be adjusted b...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.