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      Argument-based inductive logics, with coverage of compromised perception

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          Abstract

          Formal deductive logic, used to express and reason over declarative, axiomatizable content, captures, we now know, essentially all of what is known in mathematics and physics, and captures as well the details of the proofs by which such knowledge has been secured. This is certainly impressive, but deductive logic alone cannot enable rational adjudication of arguments that are at variance (however much additional information is added). After affirming a fundamental directive, according to which argumentation should be the basis for human-centric AI, we introduce and employ both a deductive and—crucially—an inductive cognitive calculus. The former cognitive calculus, DCEC , is the deductive one and is used with our automated deductive reasoner ShadowProver; the latter, IDCEC , is inductive, is used with the automated inductive reasoner ShadowAdjudicator, and is based on human-used concepts of likelihood (and in some dialects of IDCEC , probability). We explain that ShadowAdjudicator centers around the concept of competing and nuanced arguments adjudicated non-monotonically through time. We make things clearer and more concrete by way of three case studies, in which our two automated reasoners are employed. Case Study 1 involves the famous Monty Hall Problem. Case Study 2 makes vivid the efficacy of our calculi and automated reasoners in simulations that involve a cognitive robot (PERI.2). In Case Study 3, as we explain, the simulation employs the cognitive architecture ARCADIA, which is designed to computationally model human-level cognition in ways that take perception and attention seriously. We also discuss a type of argument rarely analyzed in logic-based AI; arguments intended to persuade by leveraging human deficiencies. We end by sharing thoughts about the future of research and associated engineering of the type that we have displayed.

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                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                08 January 2024
                2023
                : 6
                : 1144569
                Affiliations
                [1] 1Rensselaer AI & Reasoning (RAIR) Lab, Department of Computer Science, Department of Cognitive Science, Rensselaer Polytechnic Institute , Troy, NY, United States
                [2] 2Naval Research Laboratory , Washington, DC, United States
                [3] 3College of Information Sciences and Technology, Pennsylvania State University , State College, PA, United States
                Author notes

                Edited by: Loizos Michael, Open University of Cyprus, Cyprus

                Reviewed by: Ute Schmid, University of Bamberg, Germany

                Tarek R. Besold, Eindhoven University of Technology, Netherlands

                *Correspondence: Selmer Bringsjord selmerbringsjord@ 123456gmail.com
                Article
                10.3389/frai.2023.1144569
                10800596
                38259824
                488d354e-5199-4079-97ba-5dce5db3e9b2
                Copyright © 2024 Bringsjord, Giancola, Govindarajulu, Slowik, Oswald, Bello and Clark.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 January 2023
                : 04 October 2023
                Page count
                Figures: 10, Tables: 1, Equations: 11, References: 119, Pages: 27, Words: 22642
                Categories
                Artificial Intelligence
                Hypothesis and Theory
                Custom metadata
                Machine Learning and Artificial Intelligence

                inductive logic,compromised perception,argument and automated reasoning,monty hall dilemma,cognitive robotics,ai

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