Automated Reasoning
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Deduction Systems Based on Resolution, We Limit the Following Considerations to Transition Systems Based on Analytic Calculi
Author’s Address Norbert Eisinger European Computer–Industry Research Centre, ECRC Arabellastr. 17 D-8000 M¨unchen 81 F. R. Germany [email protected] and Hans J¨urgen Ohlbach Max–Planck–Institut f¨ur Informatik Im Stadtwald D-6600 Saarbr¨ucken 11 F. R. Germany [email protected] Publication Notes This report appears as chapter 4 in Dov Gabbay (ed.): ‘Handbook of Logic in Artificial Intelligence and Logic Programming, Volume I: Logical Foundations’. It will be published by Oxford University Press, 1992. Fragments of the material already appeared in chapter two of Bl¨asis & B¨urckert: Deduction Systems in Artificial Intelligence, Ellis Horwood Series in Artificial Intelligence, 1989. A draft version has been published as SEKI Report SR-90-12. The report is also published as an internal technical report of ECRC, Munich. Acknowledgements The writing of the chapters for the handbook has been a highly coordinated effort of all the people involved. We want to express our gratitude for their many helpful contributions, which unfortunately are impossible to list exhaustively. Special thanks for reading earlier drafts and giving us detailed feedback, go to our second reader, Bob Kowalski, and to Wolfgang Bibel, Elmar Eder, Melvin Fitting, Donald W. Loveland, David Plaisted, and J¨org Siekmann. Work on this chapter started when both of us were members of the Markgraf Karl group at the Universit¨at Kaiserslautern, Germany. With our former colleagues there we had countless fruitful discus- sions, which, again, cannot be credited in detail. During that time this research was supported by the “Sonderforschungsbereich 314, K¨unstliche Intelligenz” of the Deutsche Forschungsgemeinschaft (DFG). -
Why Machines Don't
KI - Künstliche Intelligenz https://doi.org/10.1007/s13218-019-00599-w SURVEY Why Machines Don’t (yet) Reason Like People Sangeet Khemlani1 · P. N. Johnson‑Laird2,3 Received: 15 February 2019 / Accepted: 19 June 2019 © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019 Abstract AI has never come to grips with how human beings reason in daily life. Many automated theorem-proving technologies exist, but they cannot serve as a foundation for automated reasoning systems. In this paper, we trace their limitations back to two historical developments in AI: the motivation to establish automated theorem-provers for systems of mathematical logic, and the formulation of nonmonotonic systems of reasoning. We then describe why human reasoning cannot be simulated by current machine reasoning or deep learning methodologies. People can generate inferences on their own instead of just evaluating them. They use strategies and fallible shortcuts when they reason. The discovery of an inconsistency does not result in an explosion of inferences—instead, it often prompts reasoners to abandon a premise. And the connectives they use in natural language have diferent meanings than those in classical logic. Only recently have cognitive scientists begun to implement automated reasoning systems that refect these human patterns of reasoning. A key constraint of these recent implementations is that they compute, not proofs or truth values, but possibilities. Keywords Reasoning · Mental models · Cognitive models 1 Introduction problems for mathematicians). Their other uses include the verifcation of computer programs and the design of com- The great commercial success of deep learning has pushed puter chips. -
An Overview of Automated Reasoning
An overview of automated reasoning John Harrison Intel Corporation 3rd February 2014 (10:30{11:30) Talk overview I What is automated reasoning? I Early history and taxonomy I Automation, its scope and limits I Interactive theorem proving I Hobbes (1651): \Reason . is nothing but reckoning (that is, adding and subtracting) of the consequences of general names agreed upon, for the marking and signifying of our thoughts." I Leibniz (1685) \When there are disputes among persons, we can simply say: Let us calculate [calculemus], without further ado, to see who is right." Nowadays, by `automatic and algorithmic' we mean `using a computer program'. What is automated reasoning? Attempting to perform logical reasoning in an automatic and algorithmic way. An old dream! I Leibniz (1685) \When there are disputes among persons, we can simply say: Let us calculate [calculemus], without further ado, to see who is right." Nowadays, by `automatic and algorithmic' we mean `using a computer program'. What is automated reasoning? Attempting to perform logical reasoning in an automatic and algorithmic way. An old dream! I Hobbes (1651): \Reason . is nothing but reckoning (that is, adding and subtracting) of the consequences of general names agreed upon, for the marking and signifying of our thoughts." Nowadays, by `automatic and algorithmic' we mean `using a computer program'. What is automated reasoning? Attempting to perform logical reasoning in an automatic and algorithmic way. An old dream! I Hobbes (1651): \Reason . is nothing but reckoning (that is, adding and subtracting) of the consequences of general names agreed upon, for the marking and signifying of our thoughts." I Leibniz (1685) \When there are disputes among persons, we can simply say: Let us calculate [calculemus], without further ado, to see who is right." What is automated reasoning? Attempting to perform logical reasoning in an automatic and algorithmic way. -
Mutation COS 326 Speaker: Andrew Appel Princeton University
Mutation COS 326 Speaker: Andrew Appel Princeton University slides copyright 2020 David Walker and Andrew Appel permission granted to reuse these slides for non-commercial educational purposes C structures are mutable, ML structures are immutable C program OCaml program let fst(x:int,y:int) = x struct foo {int x; int y} *p; let p: int*int = ... in int a,b,u; let a = fst p in a = p->x; let u = f p in u = f(p); let b = fst p in b = p->x; xxx (* does a==b? Yes! *) /* does a==b? maybe */ 2 Reasoning about Mutable State is Hard mutable set immutable set insert i s1; let s1 = insert i s0 in f x; f x; member i s1 member i s1 Is member i s1 == true? … – When s1 is mutable, one must look at f to determine if it modifies s1. – Worse, one must often solve the aliasing problem. – Worse, in a concurrent setting, one must look at every other function that any other thread may be executing to see if it modifies s1. 3 Thus far… We have considered the (almost) purely functional subset of OCaml. – We’ve had a few side effects: printing & raising exceptions. Two reasons for this emphasis: – Reasoning about functional code is easier. • Both formal reasoning – equationally, using the substitution model – and informal reasoning • Data structures are persistent. – They don’t change – we build new ones and let the garbage collector reclaim the unused old ones. • Hence, any invariant you prove true stays true. – e.g., 3 is a member of set S. -
The Method of Variable Splitting
The Method of Variable Splitting Roger Antonsen Thesis submitted to the Faculty of Mathematics and Natural Sciences at the University of Oslo for the degree of Philosophiae Doctor (PhD) Date of submission: June 30, 2008 Date of public defense: October 3, 2008 Supervisors Prof. Dr. Arild Waaler, University of Oslo Prof. Dr. Reiner H¨ahnle, Chalmers University of Technology Adjudication committee Prof. Dr. Matthias Baaz, Vienna University of Technology Prof. emer. Dr. Wolfgang Bibel, Darmstadt University of Technology Dr. Martin Giese, University of Oslo Copyright © 2008 Roger Antonsen til mine foreldre, Karin og Ingvald Table of Contents Acknowledgments vii Chapter 1 Introduction 1 1.1 Influential Ideas in Automated Reasoning2 1.2 Perspectives on Variable Splitting4 1.3 A Short History of Variable Splitting7 1.4 Delimitations and Applicability 10 1.5 Scientific Contribution 10 1.6 A Few Words of Introduction 11 1.7 Notational Conventions and Basics 12 1.8 The Contents of the Thesis 14 Chapter 2 A Tour of Rules and Inferences 15 2.1 Ground Sequent Calculus 16 2.2 Free-variable Sequent Calculus 17 2.3 Another Type of Redundancy 19 2.4 Variable-Sharing Calculi and Variable Splitting 20 Chapter 3 Preliminaries 23 3.1 Indexing 23 3.2 Indices and Indexed Formulas 24 3.3 The -relation 28 3.4 The Basic Variable-Sharing Calculus 28 3.5 Unifiers and Provability 30 3.6 Permutations 31 3.7 Conformity and Proof Invariance 35 3.8 Semantics 37 3.9 Soundness and Completeness 40 Chapter 4 Variable Splitting 41 4.1 Introductory Examples 41 4.2 Branch Names -
Automated Reasoning for Explainable Artificial Intelligence∗
EPiC Series in Computing Volume 51, 2017, Pages 24–28 ARCADE 2017. 1st International Workshop on Automated Reasoning: Challenges, Appli- cations, Directions, Exemplary Achievements Automated Reasoning for Explainable Artificial Intelligence∗ Maria Paola Bonacina1 Dipartimento di Informatica Universit`adegli Studi di Verona Strada Le Grazie 15 I-37134 Verona, Italy, EU [email protected] Abstract Reasoning and learning have been considered fundamental features of intelligence ever since the dawn of the field of artificial intelligence, leading to the development of the research areas of automated reasoning and machine learning. This paper discusses the relationship between automated reasoning and machine learning, and more generally be- tween automated reasoning and artificial intelligence. We suggest that the emergence of the new paradigm of XAI, that stands for eXplainable Artificial Intelligence, is an oppor- tunity for rethinking these relationships, and that XAI may offer a grand challenge for future research on automated reasoning. 1 Artificial Intelligence, Automated Reasoning, and Ma- chine Learning Ever since the beginning of artificial intelligence, scientists concurred that the capability to learn and the capability to reason about problems and solve them are fundamental pillars of intelligent behavior. Similar to many subfields of artificial intelligence, automated reasoning and machine learning have grown into highly specialized research areas. Both have experienced the dichotomy between the ideal of imitating human intelligence, whereby the threshold of success would be the indistinguishability of human and machine, as in the Turing’s test [12], and the ideal that machines can and should learn and reason in their own way. According to the latter ideal, the measure of success is independent of similarity to human behavior. -
Constraint Microkanren in the Clp Scheme
CONSTRAINT MICROKANREN IN THE CLP SCHEME Jason Hemann Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the Department of School of Informatics, Computing, and Engineering Indiana University January 2020 Accepted by the Graduate Faculty, Indiana University, in partial fulfillment of the require- ments for the degree of Doctor of Philosophy. Daniel P. Friedman, Ph.D. Amr Sabry, Ph.D. Sam Tobin-Hochstadt, Ph.D. Lawrence Moss, Ph.D. December 20, 2019 ii Copyright 2020 Jason Hemann ALL RIGHTS RESERVED iii To Mom and Dad. iv Acknowledgements I want to thank all my housemates and friends from 1017 over the years for their care and support. I’m so glad to have you all, and to have you all over the world. Who would have thought that an old house in Bloomington could beget so many great memories. While I’m thinking of it, thanks to Lisa Kamen and Bryan Rental for helping to keep a roof over our head for so many years. Me encantan mis salseros y salseras. I know what happens in the rueda stays in the rueda, so let me just say there’s nothing better for taking a break from right-brain activity. Thanks to Kosta Papanicolau for his early inspiration in math, critical thinking, and research, and to Profs. Mary Flagg and Michael Larsen for subsequent inspiration and training that helped prepare me for this work. Learning, eh?—who knew? I want to thank also my astounding undergraduate mentors including Profs. -
The Ratiolog Project: Rational Extensions of Logical Reasoning
The RatioLog Project: Rational Extensions of Logical Reasoning Ulrich Furbach · Claudia Schon · Frieder Stolzenburg · Karl-Heinz Weis · Claus-Peter Wirth Abstract Higher-level cognition includes logical reasoning 1 Rational Reasoning and Question Answering and the ability of question answering with common sense. The RatioLog project addresses the problem of rational rea- The development of formal logic played a big role in the soning in deep question answering by methods from auto- field of automated reasoning, which led to the development mated deduction and cognitive computing. In a first phase, of the field of artificial intelligence (AI). Applications of au- we combine techniques from information retrieval and ma- tomated deduction in mathematics have been investigated chine learning to find appropriate answer candidates from from the early years on. Nowadays automated deduction the huge amount of text in the German version of the free techniques are successfully applied in hard- and software encyclopedia “Wikipedia”. In a second phase, an automated verification and many other areas (for an overview see [2]). theorem prover tries to verify the answer candidates on In contrast to formal logical reasoning, however, human the basis of their logical representations. In a third phase reasoning does not strictly follow the rules of classical logic. — because the knowledge may be incomplete and inconsis- Reasons may be incomplete knowledge, incorrect beliefs, tent —, we consider extensions of logical reasoning to im- and inconsistent norms. From the very beginning of AI re- prove the results. In this context, we work toward the appli- search, there has been a strong emphasis on incorporating cation of techniques from human reasoning: We employ de- mechanisms for rationality, such as abductive or defeasible feasible reasoning to compare the answers w.r.t. -
Automated Reasoning on the Web
Automated Reasoning on the Web Slim Abdennadher1, Jos´eJ´ulioAlves Alferes2, Grigoris Antoniou3, Uwe Aßmann4, Rolf Backofen5, Cristina Baroglio6, Piero A. Bonatti7, Fran¸coisBry8, W lodzimierz Drabent9, Norbert Eisinger8, Norbert E. Fuchs10, Tim Geisler11, Nicola Henze12, Jan Ma luszy´nski4, Massimo Marchiori13, Alberto Martelli14, Sara Carro Mart´ınez15, Wolfgang May16, Hans J¨urgenOhlbach8, Sebastian Schaffert8, Michael Schr¨oder17, Klaus U. Schulz8, Uta Schwertel8, and Gerd Wagner18 1 German University in Cairo (Egypt), 2 New University of Lisbon (Portugal), 3 Institute of Computer Science, Foundation for Research and Technology – Hel- las (Greece), 4 University of Link¨oping (Sweden), 5 University of Jena (Germany), 6 University of Turin (Italy), 7 University of Naples (Italy), 8 University of Mu- nich (Germany), 9 Polish Academy of Sciences (Poland), 10 University of Zurich (Switzerland), 11 webXcerpt (Germany), 12 University of Hannover (Germany), 13 University of Venice (Italy), 14 University of Turin (Italy), 15 Telef´onica Investi- gaci´ony Desarollo (Spain), 16 University of G¨ottingen(Germany), 17 University of Dresden (Germany), 18 University of Eindhoven (The Netherlands) Abstract Automated reasoning is becoming an essential issue in many Web systems and applications, especially in emerging Semantic Web applications. This article first discusses reasons for this evolution. Then, it presents research issues currently investigated towards automated reasoning on the Web and it introduces into selected applications demonstrating the practical impact of the approach. Finally, it introduces a research endeavor called REWERSE (cf. http://rewerse.net) recently launched by the authors of this article which is concerned with developing automated reasoning methods and tools for the Web as well as demonstrator applications. -
Development of Logic Programming: What Went Wrong, What Was Done About It, and What It Might Mean for the Future
Development of Logic Programming: What went wrong, What was done about it, and What it might mean for the future Carl Hewitt at alum.mit.edu try carlhewitt Abstract follows: A class of entities called terms is defined and a term is an expression. A sequence of expressions is an Logic Programming can be broadly defined as “using logic expression. These expressions are represented in the to deduce computational steps from existing propositions” machine by list structures [Newell and Simon 1957]. (although this is somewhat controversial). The focus of 2. Certain of these expressions may be regarded as this paper is on the development of this idea. declarative sentences in a certain logical system which Consequently, it does not treat any other associated topics will be analogous to a universal Post canonical system. related to Logic Programming such as constraints, The particular system chosen will depend on abduction, etc. programming considerations but will probably have a The idea has a long development that went through single rule of inference which will combine substitution many twists in which important questions turned out to for variables with modus ponens. The purpose of the combination is to avoid choking the machine with special have surprising answers including the following: cases of general propositions already deduced. Is computation reducible to logic? 3. There is an immediate deduction routine which when Are the laws of thought consistent? given a set of premises will deduce a set of immediate This paper describes what went wrong at various points, conclusions. Initially, the immediate deduction routine what was done about it, and what it might mean for the will simply write down all one-step consequences of the future of Logic Programming. -
Frege's Influence on the Modern Practice of Doing Mathematics
논리연구 20-1(2017) pp. 97-112 Frege’s influence on the modern practice of doing mathematics 1) Gyesik Lee 【Abstract】We discuss Frege’s influence on the modern practice of doing mathematical proofs. We start with explaining Frege’s notion of variables. We also talk of the variable binding issue and show how successfully his idea on this point has been applied in the field of doing mathematics based on a computer software. 【Key Words】Frege, Begriffsschrift, variable binding, mechanization, formal proofs Received: Nov. 11, 2016. Revised: Feb. 10, 2017. Accepted: Feb. 11, 2017. 98 Gyesik Lee 1. Introduction Around the turn of the 20th century, mathematicians and logicians were interested in a more exact investigation into the foundation of mathematics and soon realized that ordinary mathematical arguments can be represented in formal axiomatic systems. One prominent figure in this research was Gottlob Frege. His main concern was twofold: (1) whether arithmetical judgments could be proved in a purely logical manner, and (2) how far one could progress in arithmetic merely by using the laws of logic. In Begriffsschrift (Frege, 1879), he invented a special kind of language system, where statements can be proved as true based only upon some general logical laws and definitions. This system is then used later in the two volumes of Grundgesetze der Arithmetik (Frege, 1893, 1903), where he provided and analyzed a formal system for second order arithmetic. Although the system in (Frege, 1893, 1903) is known to be inconsistent, it contains all of the essential materials to provide a fundamental basis for dealing with propositions of arithmetic based on an axiomatic system. -
Reasoning-Driven Question-Answering for Natural Language Understanding Daniel Khashabi University of Pennsylvania, [email protected]
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2019 Reasoning-Driven Question-Answering For Natural Language Understanding Daniel Khashabi University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Artificial Intelligence and Robotics Commons Recommended Citation Khashabi, Daniel, "Reasoning-Driven Question-Answering For Natural Language Understanding" (2019). Publicly Accessible Penn Dissertations. 3271. https://repository.upenn.edu/edissertations/3271 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/3271 For more information, please contact [email protected]. Reasoning-Driven Question-Answering For Natural Language Understanding Abstract Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning.