This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Introduction There is mounting psychological evidence that human cognition centrally involves similarity computations over structured representations, in tasks ranging from high-level visual perception to problem solving, learning, and conceptual change . Understanding how to integrate analogical processing into AI systems seems crucial to creating more humanlike reasoning systems .
This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Queries that include analogical operations can be formulated in the same way as standard logical inference, and analogical processing systems in turn can call on the services of first-principles reasoners for creating cases and validating their conjectures. We illustrate the utility of the analogy ontology by demonstrating how it has been used in three systems: A crisis management analogical reasoner that answers questions about international incidents, a course of action analogical critiquer that provides feedback about military plans, and a comparison question-answering system for knowledge capture.
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Moral reasoning is important to accurately model as AI systems become ever more integrated into our lives. Moral reasoning is rapid and unconscious; analogical reasoning, which can be unconscious, is a promising approach to model moral reasoning. This paper explores the use of analogical generalizations to improve moral reasoning. Analogical reasoning has already been used to successfully model moral reasoning in the MoralDM model, but it exhaustively matches across all known cases, which is computationally intractable and cognitively implausible for human-scale knowledge bases. We investigate the performance of an extension of MoralDM to use the MAC/FAC model of analogical retrieval over three conditions, across a set of highly confusable moral scenarios.
Artificial Intelligence (AI) refers to the art or ability of computers to function the way human intelligence does in terms of learning, reasoning, decision-making and problem-solving. Systems integrated with Artificial Intelligence solutions are capable of understanding languages, recognizing images, performing complex reasoning and decision-making involving the most crucial mathematical parameters. By deploying the appropriate AI technology, your business may see new heights in terms of agility. With the advanced AI-powered applications, businesses are now seeing exponential growths and are driving the system in the most efficient manner. AI services have the potential to change the way businesses work today to enhance operational efficiency & fuelling business growth.