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Automated reasoning in classical logic has received much attention in the literature. Mature resolution theorem provers such as Vampire and E can handle enormous problems in first-order classical logic with equality. Waldmeister, a theorem prover for unit equational logic, has been incorporated into Mathematica as an equational reasoning method. Somewhat surprisingly, there has been much less attention devoted to non-classical logics. This is unfortunate, since many interesting and useful logics are inherently non-classical. Well known examples include intuitionistic, substructural and modal logics. Additionally, many modern logics for specialized tasks such as those designed for security and authentication protocols are non-classical as well. The inverse method is a generalization of resolution to non-classical logics. The polarized inverse method extends the inverse method with focusing and explicit polarity assignments that dramatically decrease the search space. In this talk we will suggest that the polarized inverse method provides an efficient and flexible framework for implementing theorem provers for non-classical logics. As evidence, we will demonstrate our recent theorem prover for intuitionistic logic, named Imogen. Imogen performs better than any other intuitionistic prover on standard benchmarks. We will also discuss recent work extending Imogen to handle domain-specific reasoning such as Microsoft's Infon logic. Finally, we will introduce a recent technique for encoding substructural logics into intuitionistic logic, thus yielding theorem provers for these logics 'for free'. ©2009 Microsoft Corporation. All rights reserved.

Journal of Automated Reasoning 1998: Volume 20 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1998_20_index . Next issue: sim_journal-of-automated-reasoning_1998-04_20_1-2 .

Our objective in this note is to comment briefly on the newly emerging literature on computer-aided proofs in Social Choice Theory. We shall specifically comment on two papers, one by Tang and Lin (2009) and another by Geist and Endriss (2011). We also provide statements and brief descriptions of the results discussed in this note.

Software is everywhere, from mission critical systems such as industrial power stations, pacemakers and even common household appliances we are surrounded by software with potentially exploitable vulnerabilities. The growing complexity of software, the rise in IoT devices coupled with our dependence on technology has made program analysis more specifically binary analysis an important area of research in computer science. Moreover these needs and dependencies have made it a necessity to explore building automated analysis systems that can operate at scale, speed and efficacy all while performing with the skill of a human expert. Though great progress has been made in this area of research, there remains limitations and open challenges to be addressed. Recognizing this need, DARAP sponsored the Cyber Grand Challenge (CGC), a competition to showcase the current state of the art in systems that perform; automated vulnerability detection, exploit generation and software patching. This paper is a survey of the vulnerability detection and exploit generation techniques, underlying technologies and related works of two of the winning systems Mayhem and Mechanical Phish.

The assurance of the safety or security of critical software rests on a clear understanding of the formal semantics of the programming language used. Operational semantics is the most widely used means of formally defining a language. The need for high levels of assurance, along with the complexity of these definitions for real programming languages, means that tool support is essential for carrying out reasoning about code with respect to the language definition. In this paper, we describe a generic approach to automated reasoning about the operational semantics of programming languages. As an application of this approach, we describe the construction of an environment for reasoning about programs written in a functional subset of ML. The system we describe (called Elle) captures the formal operational semantics definition of a large subset of Standard ML within the theorem prover Isabelle, and provides some support for the verification of ML programs. (AN)

In recent years, researchers in artificial intelligence have become interested in replicating human physical reasoning talents in computers. One of the most important skills in this area is predicting how physical systems will behave. This thesis discusses an implemented program that generates algebraic descriptions of how systems of rigid bodies evolve over time. Discussion about the design of this program identifies a physical reasoning paradigm and knowledge representation approach based on mathematical model construction and algebraic reasoning. This paradigm offers several advantages over methods that have become popular in the field, and seems promising for reasoning about a wide variety of classical mechanics problems. (AN)

The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.

Journal of Automated Reasoning 2001: Volume 26 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2001_26_index . Next issue: sim_journal-of-automated-reasoning_2001-01_26_1 .

Journal of Automated Reasoning 2005: Volume 35 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2005_34_4 . Next issue: sim_journal-of-automated-reasoning_2005_35_contents .

Journal of Automated Reasoning 1998: Volume 21 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1998-06_20_3 . Next issue: sim_journal-of-automated-reasoning_1998_21_contents .

Journal of Automated Reasoning 2003: Volume 31 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2003_31_index . Next issue: sim_journal-of-automated-reasoning_2003_31_1 .

Journal of Automated Reasoning 2003: Volume 31 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2003_31_index . Next issue: sim_journal-of-automated-reasoning_2003_31_1 .

This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnTPTP are the use of the Mizar language that is oriented towards human mathematicians (rather than the pure first-order logic used in SystemOnTPTP), and setting the service in the context of the large Mizar Mathematical Library of previous theorems,definitions, and proofs (rather than the isolated problems that are solved in SystemOnTPTP). These differences poses new challenges and new opportunities for automated reasoning and for proof presentation tools. This paper describes the overall structure of MizAR, and presents the automated reasoning systems and proof presentation tools that are combined to make MizAR a useful mathematical service.

The TPTP World is a well known and established infrastructure that supports research, development, and deployment of Automated Theorem Proving (ATP) systems for classical logics. The data, standards, and services provided by the TPTP World have made it increasingly easy to build, test, and apply ATP technology. This talk reviews the core features of the TPTP World, describes key service components of the TPTP World, presents some successful applications, and gives an overview of the most recent developments. ©2011 Microsoft Corporation. All rights reserved.

The TPTP World is a well known and established infrastructure that supports research, development, and deployment of Automated Theorem Proving (ATP) systems for classical logics. The data, standards, and services provided by the TPTP World have made it increasingly easy to build, test, and apply ATP technology. This talk reviews the core features of the TPTP World, describes key service components of the TPTP World, presents some successful applications, and gives an overview of the most recent developments. ©2011 Microsoft Corporation. All rights reserved.

Several recent and fundamental advances have greatly increased the power of automated reasoning tools. Using these advances, Microsoft developed software such as Static Driver Verifier for finding bugs in device drivers. But automated reasoning libraries can be used in contexts beyond bug-finding. Examples include configuration management, scheduling, compilation, and other mathematics-based applications. In this talk I will provide information on the automated reasoning libraries that are currently available and describe how developers can use them to solve a wide variety of problems that require reasoning. I will also outline the projects within MSR to develop automated reasoning tools. ©2005 Microsoft Corporation. All rights reserved.

Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in particular use heterogeneous data sources combined with problem solving expertise in order to make critical decisions that may lead to some form of actions e.g., driver notifications, change of traffic light signals and braking to prevent an accident. Currently, a major part of the decision process is done by human domain experts, which is time-consuming, tedious and error-prone. Additionally, due to the intrinsic nature of knowledge possession this decision process cannot be easily replicated or reused. Therefore, there is a need for automating the reasoning processes by providing computational systems a formal representation of the domain knowledge and a set of methods to process that knowledge. In this paper, we propose a knowledge model that can be used to express both declarative knowledge about the systems' components, their relations and their current state, as well as procedural knowledge representing possible system behavior. In addition, we introduce a framework for knowledge management and automated reasoning (KMARF). The idea behind KMARF is to automatically select an appropriate problem solver based on formalized reasoning expertise in the knowledge base, and convert a problem definition to the corresponding format. This approach automates reasoning, thus reducing operational costs, and enables reusability of knowledge and methods across different domains. We illustrate the approach on a transportation planning use case.

Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in particular use heterogeneous data sources combined with problem solving expertise in order to make critical decisions that may lead to some form of actions e.g., driver notifications, change of traffic light signals and braking to prevent an accident. Currently, a major part of the decision process is done by human domain experts, which is time-consuming, tedious and error-prone. Additionally, due to the intrinsic nature of knowledge possession this decision process cannot be easily replicated or reused. Therefore, there is a need for automating the reasoning processes by providing computational systems a formal representation of the domain knowledge and a set of methods to process that knowledge. In this paper, we propose a knowledge model that can be used to express both declarative knowledge about the systems' components, their relations and their current state, as well as procedural knowledge representing possible system behavior. In addition, we introduce a framework for knowledge management and automated reasoning (KMARF). The idea behind KMARF is to automatically select an appropriate problem solver based on formalized reasoning expertise in the knowledge base, and convert a problem definition to the corresponding format. This approach automates reasoning, thus reducing operational costs, and enables reusability of knowledge and methods across different domains. We illustrate the approach on a transportation planning use case.

Journal of Automated Reasoning 1987: Volume 3 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1986-12_2_4 . Next issue: sim_journal-of-automated-reasoning_1987_3_contents .

Journal of Automated Reasoning 1993: Volume 11 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1993_11_index . Next issue: sim_journal-of-automated-reasoning_1993-08_11_1 .

Journal of Automated Reasoning 1993: Volume 11 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1993_11_index . Next issue: sim_journal-of-automated-reasoning_1993-08_11_1 .

This note reports on some experiments, using a handful of standard automated reasoning tools, for exploring Steinitz-Rademacher polyhedra, which are models of a certain first-order theory of incidence structures. This theory and its models, even simple ones, presents significant, geometrically fascinating challenges for automated reasoning tools.

This note reports on some experiments, using a handful of standard automated reasoning tools, for exploring Steinitz-Rademacher polyhedra, which are models of a certain first-order theory of incidence structures. This theory and its models, even simple ones, presents significant, geometrically fascinating challenges for automated reasoning tools.

STAR is an interactive, interpreted programming language for the development and operation of Artificial Intelligence application systems. The language is intended for use primarily in the development of software application systems which rely on a combination of symbolic processing, central to the vast majority of AI algorithms, with routines and data structures defined in compiled languages such as C, FORTRAN and PASCAL. References to routines and data structures defined in compiled languages are intermixed with symbolic structures in STAR, resulting in a hybrid operating environment in which symbolic and non-symbolic processing and organization of data may interact to a high degree within the execution of particular application systems. The STAR language was developed in the course of a project involving AI techniques in the interpretation of imaging spectrometer data and is derived in part from a previous language called CLIP. The interpreter for STAR is implemented as a program defined in the language C and has been made available for distribution in source code form through NASA's Computer Software Management and Information Center (COSMIC). Contained within this report are the STAR Tutorial Guide, which introduces the language in a step-by-step manner, and the STAR Reference Manual, which provides a detailed summary of the features of STAR.

Journal of Automated Reasoning 2004: Volume 33 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2004_33_index . Next issue: sim_journal-of-automated-reasoning_2004_33_1 .

Journal of Automated Reasoning 1987: Volume 3 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1987_3_index . Next issue: sim_journal-of-automated-reasoning_1987-03_3_1 .

Journal of Automated Reasoning 1990: Volume 6 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1989-12_5_4 . Next issue: sim_journal-of-automated-reasoning_1990_6_contents .

Journal of Automated Reasoning 1995: Volume 14 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1995_14_index . Next issue: sim_journal-of-automated-reasoning_1995-02_14_1 .

Journal of Automated Reasoning 1994: Volume 13 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1994-06_12_3 . Next issue: sim_journal-of-automated-reasoning_1994_13_contents .

The planetary spacecraft mission OPS as applied to SHARP is studied. Knowledge systems involved in this study are detailed. SHARP development task and Voyager telecom link analysis were examined. It was concluded that artificial intelligence has a proven capability to deliver useful functions in a real time space flight operations environment. SHARP has precipitated major change in acceptance of automation at JPL. The potential payoff from automation using AI is substantial. SHARP, and other AI technology is being transferred into systems in development including mission operations automation, science data systems, and infrastructure applications.

Includes bibliographical references and index

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book.

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book.

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book.

Combined Arms Analysis Tool for the 21st Century (COMBATXXI) is the primary analytical combat simulation tool used by the Marine Corp's Operations Analysis Directorate (OAD) and The Research Analysis Center (TRAC) under the new U.S. Army Futures Command (AFC). While the simulation has considerable capabilities and has been used for diverse studies, it has two major shortcomings that can be addressed by innovative model development techniques. The first weaknesses of COMBATXXI at the scenario development level is maneuver planning. An entity's maneuver is completely scripted by scenario developers. This is a time-consuming process that varies in realistic accuracy based on the developers' expertise and experience. Advanced terrain reasoning in COMBATXXI could enhance dynamic decision making by simulated entities, improve analysis of combined arms operations, and shorten the scenario development time. The second weakness is prototyping and testing new behaviors in COMBATXXI. This is a difficult process that can be improved by using a simplified surrogate environment. This thesis took one step toward addressing both shortcomings by developing a prototype maneuver planner in a surrogate environment (WOMBATXXI/Unity3d Game Engine) using an advanced terrain reasoning approach, testing the planner output through a simple face validity process of examining the realism and simplicity of the plans, and demonstrating the planner output over five distinct scenarios.

Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific models or only on parts of general models. Consequently, a system that can intelligently apply these inference algorithms to different parts of a model for fast reasoning is highly desirable. We introduce a new framework called structured factored inference (SFI) that provides the foundation for such a system. Using models encoded in a probabilistic programming language, SFI provides a sound means to decompose a model into sub-models, apply an inference algorithm to each sub-model, and combine the resulting information to answer a query. Our results show that SFI is nearly as accurate as exact inference yet retains the benefits of approximate inference methods.

Journal of Automated Reasoning 2002: Volume 29 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2002_29_index . Next issue: sim_journal-of-automated-reasoning_2002_29_1 .

Journal of Automated Reasoning 1997: Volume 18 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1997_18_index . Next issue: sim_journal-of-automated-reasoning_1997-02_18_1 .

Journal of Automated Reasoning 2005: Volume 35 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2005_35_index . Next issue: sim_journal-of-automated-reasoning_2005_35_1-3 .

Journal of Automated Reasoning 1996: Volume 17 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1996-06_16_3 . Next issue: sim_journal-of-automated-reasoning_1996_17_contents .

Journal of Automated Reasoning 2006: Volume 37 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2006_36_4 . Next issue: sim_journal-of-automated-reasoning_2006_37_contents .

Journal of Automated Reasoning 2006: Volume 37 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2006_36_4 . Next issue: sim_journal-of-automated-reasoning_2006_37_contents .

Journal of Automated Reasoning 1992: Volume 9 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1992-06_8_3 . Next issue: sim_journal-of-automated-reasoning_1992_9_contents .

Journal of Automated Reasoning 2004: Volume 33 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2004_32_4 . Next issue: sim_journal-of-automated-reasoning_2004_33_contents .

Compositional verification and abstraction are the key techniques to address the state explosion problem associated with model checking of concurrent software. A promising compositional approach is to prove properties of a system by checking properties of its components in an assume-guarantee style. This article proposes a framework for performing abstraction and assume-guarantee reasoning of concurrent C code in an incremental and fully automated fashion. The framework uses predicate abstraction to extract and refine finite state models of software and it uses an automata learning algorithm to incrementally construct assumptions for the compositional verification of the abstract models. The framework can be instantiated with different assume-guarantee rules. We have implemented our approach in the COMFORT reasoning framework and we show how COMFORT out-performs several previous software model checking approaches when checking safety properties of non-trivial concurrent programs.

Journal of Automated Reasoning 2000: Volume 24 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1999-11_23_3-4 . Next issue: sim_journal-of-automated-reasoning_2000_24_contents .

OWL 2 has been standardized by the World Wide Web Consortium (W3C) as a family of ontology languages for the Semantic Web. The most expressive of these languages is OWL 2 Full, but to date no reasoner has been implemented for this language. Consistency and entailment checking are known to be undecidable for OWL 2 Full. We have translated a large fragment of the OWL 2 Full semantics into first-order logic, and used automated theorem proving systems to do reasoning based on this theory. The results are promising, and indicate that this approach can be applied in practice for effective OWL reasoning, beyond the capabilities of current Semantic Web reasoners. This is an extended version of a paper with the same title that has been published at CADE 2011, LNAI 6803, pp. 446-460. The extended version provides appendices with additional resources that were used in the reported evaluation.

Journal of Automated Reasoning is an active peer-reviewed scholarly journal currently published semi-quarterly by Springer. Launched in 1985 by American mathematician and founding-editor Lawrence “Larry” Wos (1930–2020), it is dedicated to the theory, implementation, and applications of logical reasoning by computer. Each issue features original articles on a number of theoretical and applied topics that involve logical reasoning by computer. Published papers cover a range of automatic and interactive reasoning tools including, but not limited to, SAT and SMT solvers, automatic theorem provers based on superposition or tableaux, proof assistants, and program verifiers. Occasional special-focus issues are dedicated to a particular topic of interest or contain selected extended papers presented at relevant conferences and symposia. The Internet Archive Collection contains microfilm published between 1985 and 2007. Journal of Automated Reasoning is found online at: https://www.springer.com/journal/10817   Wikipedia: https://en.wikipedia.org/wiki/Journal_of_Automated_Reasoning   The ISSN is 0168-7433

Journal of Automated Reasoning 2002: Volume 29 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2002-05_28_4 . Next issue: sim_journal-of-automated-reasoning_2002_29_contents .

Journal of Automated Reasoning 2002: Volume 29 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2002-05_28_4 . Next issue: sim_journal-of-automated-reasoning_2002_29_contents .