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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.

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.

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

Includes bibliographical references and index

Journal of Automated Reasoning 1992: Volume 8 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1991-12_7_4 . Next issue: sim_journal-of-automated-reasoning_1992_8_contents .

Journal of Automated Reasoning 1999: Volume 22 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1998-12_21_3 . Next issue: sim_journal-of-automated-reasoning_1999_22_contents .

Journal of Automated Reasoning 2003: Volume 31 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2003_30_3-4 . Next issue: sim_journal-of-automated-reasoning_2003_31_contents .

Journal of Automated Reasoning 2006: Volume 36 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2006_36_index . Next issue: sim_journal-of-automated-reasoning_2006_36_1-2 .

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.

Journal of Automated Reasoning 2003: Volume 30 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2002_29_3-4 . Next issue: sim_journal-of-automated-reasoning_2003_30_contents .

Journal of Automated Reasoning 2007: Volume 39 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2007_39_index . Next issue: sim_journal-of-automated-reasoning_2007_39_1 .

Journal of Automated Reasoning 1986: Volume 2 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1985_1_4 . Next issue: sim_journal-of-automated-reasoning_1986_2_contents .

Journal of Automated Reasoning 1991: Volume 7 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1990-12_6_4 . Next issue: sim_journal-of-automated-reasoning_1991_7_contents .

This talk describes how a combination of symbolic computation techniques with first-order theorem proving can be used for solving some challenges of automating program analysis, in particular for generating and proving properties about the logically complex parts of software. The talk will first present how computer algebra methods, such as Groebner basis computation, quantifier elimination and algebraic recurrence solving, help us in inferring properties of program loops with non-trivial arithmetic. Typical properties inferred by our work are loop invariants and expressions bounding the number of loop iterations. The talk will then describe our work to generate first-order properties of programs with unbounded data structures, such as arrays. For doing so, we use saturation-based first-order theorem proving and extend first-order provers with support for program analysis. Since program analysis requires reasoning in the combination of first-order theories of data structures, the talk also discusses new features in firstorder theorem proving, such as inductive reasoning and built-in boolean sort. These extensions allow us to express program properties directly in first-order logic and hence use further first-order theorem provers to reason about program properties.

This talk describes how a combination of symbolic computation techniques with first-order theorem proving can be used for solving some challenges of automating program analysis, in particular for generating and proving properties about the logically complex parts of software. The talk will first present how computer algebra methods, such as Groebner basis computation, quantifier elimination and algebraic recurrence solving, help us in inferring properties of program loops with non-trivial arithmetic. Typical properties inferred by our work are loop invariants and expressions bounding the number of loop iterations. The talk will then describe our work to generate first-order properties of programs with unbounded data structures, such as arrays. For doing so, we use saturation-based first-order theorem proving and extend first-order provers with support for program analysis. Since program analysis requires reasoning in the combination of first-order theories of data structures, the talk also discusses new features in firstorder theorem proving, such as inductive reasoning and built-in boolean sort. These extensions allow us to express program properties directly in first-order logic and hence use further first-order theorem provers to reason about program properties.

Journal of Automated Reasoning 1993: Volume 10 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1993_10_index . Next issue: sim_journal-of-automated-reasoning_1993-02_10_1 .

Journal of Automated Reasoning 1996: Volume 17 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1996_17_index . Next issue: sim_journal-of-automated-reasoning_1996-08_17_1 .

Journal of Automated Reasoning 2001: Volume 27 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2001_27_index . Next issue: sim_journal-of-automated-reasoning_2001-07_27_1 .

Journal of Automated Reasoning 2002: Volume 28 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2002_28_index . Next issue: sim_journal-of-automated-reasoning_2002-01_28_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.

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.

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

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.

Information technology is now ubiquitous in higher education institutions worldwide. More than 85% of American universities use e-learning systems to supplement traditional classroom activities while some have started offering Massive Online Open Courses (MOOCs), which are completely online. An obvious benefit of these online tools is their ability to automatically grade exercises submitted by students and provide immediate feedback. Most of these systems, however, provide binary ("Correct/Incorrect") feedback to students. While such feedback is useful, some students may need additional guidance in order to successfully overcome obstacles to understanding. We propose using a Case-Based Reasoning (CBR) approach to improve the quality of feedback Computer Science students receive on their programming exercises. CBR is a machine learning technique that attempts to solve problems based on previous experiences (cases). The basic idea is that every time the instructor provides feedback to a student on a particular exercise, the information is stored in a database system as a past case. When student experience similar problems in the future, knowledge contained in past cases is used to guide the students to a solution. While the system will provide detailed feedback automatically, this feedback will have been previously crafted by human instructors, leveraging their pedagogical expertise. We describe a system of this kind, which is currently under development, and we report results from a preliminary experiment. [For full proceedings, see ED557189.]

A general-purpose computer program for planning the actions of a spacecraft or other complex system has been augmented by incorporating a subprogram that reasons about uncertainties in such continuous variables as times taken to perform tasks and amounts of resources to be consumed. This subprogram computes parametric probability distributions for time and resource variables on the basis of user-supplied models of actions and resources that they consume. The current system accepts bounded Gaussian distributions over action duration and resource use. The distributions are then combined during planning to determine the net probability distribution of each resource at any time point. In addition to a full combinatoric approach, several approximations for arriving at these combined distributions are available, including maximum-likelihood and pessimistic algorithms. Each such probability distribution can then be integrated to obtain a probability that execution of the plan under consideration would violate any constraints on the resource. The key idea is to use these probabilities of conflict to score potential plans and drive a search toward planning low-risk actions. An output plan provides a balance between the user s specified averseness to risk and other measures of optimality.

A general-purpose computer program for planning the actions of a spacecraft or other complex system has been augmented by incorporating a subprogram that reasons about uncertainties in such continuous variables as times taken to perform tasks and amounts of resources to be consumed. This subprogram computes parametric probability distributions for time and resource variables on the basis of user-supplied models of actions and resources that they consume. The current system accepts bounded Gaussian distributions over action duration and resource use. The distributions are then combined during planning to determine the net probability distribution of each resource at any time point. In addition to a full combinatoric approach, several approximations for arriving at these combined distributions are available, including maximum-likelihood and pessimistic algorithms. Each such probability distribution can then be integrated to obtain a probability that execution of the plan under consideration would violate any constraints on the resource. The key idea is to use these probabilities of conflict to score potential plans and drive a search toward planning low-risk actions. An output plan provides a balance between the user s specified averseness to risk and other measures of optimality.

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.

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.

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 2007: Volume 38 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2006_37_4 . Next issue: sim_journal-of-automated-reasoning_2007_38_contents .

This dissertation explores the roles of polarities and focussing in various aspects of Computational Logic. These concepts play a key role in the the interpretation of proofs as programs, a.k.a. the Curry-Howard correspondence, in the context of classical logic. Arising from linear logic, they allow the construction of meaningful semantics for cut-elimination in classical logic, some of which relate to the Call-by-Name and Call-by-Value disciplines of functional programming. The first part of this dissertation provides an introduction to these interpretations, highlighting the roles of polarities and focussing. For instance: proofs of positive formulae provide structured data, while proofs of negative formulae consume such data; focussing allows the description of the interaction between the two kinds of proofs as pure pattern-matching. This idea is pushed further in the second part of this dissertation, and connected to realisability semantics, where the structured data is interpreted algebraically, and the consumption of such data is modelled with the use of an orthogonality relation. Most of this part has been proved in the Coq proof assistant. Polarities and focussing were also introduced with applications to logic programming in mind, where computation is proof-search. In the third part of this dissertation, we push this idea further by exploring the roles that these concepts can play in other applications of proof-search, such as theorem proving and more particularly automated reasoning. We use these concepts to describe the main algorithm of SAT-solvers and SMT-solvers: DPLL. We then describe the implementation of a proof-search engine called Psyche. Its architecture, based on the concept of focussing, offers a platform where smart techniques from automated reasoning (or a user interface) can safely and trustworthily be implemented via the use of an API.

We develop a learning-based automated Assume-Guarantee (AG) reasoning framework for verifying omega-regular properties of concurrent systems. We study the applicability of non-circular (AGNC) and circular (AG-C) AG proof rules in the context of systems with infinite behaviors. In particular, we show that AG-NC is incomplete when assumptions are restricted to strictly infinite behaviors, while AG-C remains complete. We present a general formalization, called LAG, of the learning based automated AG paradigm. We show how existing approaches for automated AG reasoning are special instances of LAG.We develop two learning algorithms for a class of systems, called infinite regular systems, that combine finite and infinite behaviors. We show that for infinity-regular systems, both AG-NC and AG-C are sound and complete. Finally, we show how to instantiate LAG to do automated AG reasoning for infinite regular, and omega-regular, systems using both AG-NC and AG-C as proof rules

We develop a learning-based automated Assume-Guarantee (AG) reasoning framework for verifying omega-regular properties of concurrent systems. We study the applicability of non-circular (AGNC) and circular (AG-C) AG proof rules in the context of systems with infinite behaviors. In particular, we show that AG-NC is incomplete when assumptions are restricted to strictly infinite behaviors, while AG-C remains complete. We present a general formalization, called LAG, of the learning based automated AG paradigm. We show how existing approaches for automated AG reasoning are special instances of LAG.We develop two learning algorithms for a class of systems, called infinite regular systems, that combine finite and infinite behaviors. We show that for infinity-regular systems, both AG-NC and AG-C are sound and complete. Finally, we show how to instantiate LAG to do automated AG reasoning for infinite regular, and omega-regular, systems using both AG-NC and AG-C as proof rules

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 .

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 2007: Volume 39 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2007_38_4 . Next issue: sim_journal-of-automated-reasoning_2007_39_contents .

Journal of Automated Reasoning 2007: Volume 39 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_2007_38_4 . Next issue: sim_journal-of-automated-reasoning_2007_39_contents .

The likelihood of an automated reasoning program being of substantial assistance for a wide spectrum of applications rests with the nature of the options and parameters it offers on which to base needed strategies and methodologies. This article focuses on such a spectrum, featuring W. McCune's program OTTER, discussing widely varied successes in answering open questions, and touching on some of the strategies and methodologies that played a key role. The applications include finding a first proof, discovering single axioms, locating improved axiom systems, and simplifying existing proofs. The last application is directly pertinent to the recently found (by R. Thiele) Hilbert's twenty-fourth problem--which is extremely amenable to attack with the appropriate automated reasoning program--a problem concerned with proof simplification. The methodologies include those for seeking shorter proofs and for finding proofs that avoid unwanted lemmas or classes of term, a specific option for seeking proofs with smaller equational or formula complexity, and a different option to address the variable richness of a proof. The type of proof one obtains with the use of OTTER is Hilbert-style axiomatic, including details that permit one sometimes to gain new insights. We include questions still open and challenges that merit consideration.

Nestan Tsiskaridze, University of California, Santa Barbara The modern world critically depends on the security and safety of software. We seek to ensure customer confidence and protect privacy, intellectual property, and national security. As threats to software security have become more sophisticated, so too have the techniques developed to ensure security. This talk focuses on novel opportunities to automate bug detection and security exploit generation provided by advances in symbolic execution and automated constraint solving. It discusses how symbolic execution can benefit from novel techniques in Satisfiability Modulo Theories (SMT), a subfield of automated theorem proving that in the past 10 years has revolutionized the discipline. The talk presents a recent highly successful application of SMT solvers in support of the security analysis of Web applications and how these new capabilities open opportunities for automating such analysis beyond the Web. This is a joint work with Clark Barrett (NYU/Stanford University), Morgan Deters (NYU), Tianyi Liang (The University of Iowa), Andrew Reynolds (The University of Iowa/EPFL), and Cesare Tinelli (The University of Iowa). Sign up to find out more about Enigma conferences: https://www.usenix.org/conference/enigma2017#signup Watch all Enigma 2017 videos at: http://enigma.usenix.org/youtube Source: https://www.youtube.com/watch?v=wLlnKu4HmaU Uploader: USENIX Enigma Conference Upload date: 2017-03-06

This project aimed to make significant advances towards enabling more adaptive systems involving human and machine components by characterizing system behavior as the result of a reasoning process. Rather than specifying every operation in advance, this approach only requires one to provide the system with its overall goals in addition to some knowledge of the environment, its dynamics and the effects of its actions. In unanticipated or troublesome situations, the system would adapt its behavior by reasoning about the appropriate actions to take to achieve its goals. During this work we helped enable a system that took vague and incomplete commands from a human user and performed the correct action. This was enabled by using a reasoning engine to resolve the ambiguities and infer missing information from the command. The technical challenges was that current inference engines did not scale to problems of this size. We made several advances that enabled them to be used on such problems and demonstrated them on working systems that displayed a notable increase in the abilities of computers to understand natural language commands.

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.

Deontic logic is a very well researched branch of mathematical logic and philosophy. Various kinds of deontic logics are discussed for different application domains like argumentation theory, legal reasoning, and acts in multi-agent systems. In this paper, we show how standard deontic logic can be stepwise transformed into description logic and DL- clauses, such that it can be processed by Hyper, a high performance theorem prover which uses a hypertableau calculus. Two use cases, one from multi-agent research and one from the development of normative system are investigated.

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 1994: Volume 13 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1994_13_index . Next issue: sim_journal-of-automated-reasoning_1994-08_13_1 .

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 1995: Volume 14 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1994-12_13_3 . Next issue: sim_journal-of-automated-reasoning_1995_14_contents .

Journal of Automated Reasoning 1995: Volume 15 , Issue CONTENTS. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1995_15_index . Next issue: sim_journal-of-automated-reasoning_1995-08_15_1 .

Journal of Automated Reasoning 1998: Volume 20 , Issue Index. Digitized from IA1513825-03 . Previous issue: sim_journal-of-automated-reasoning_1997-12_19_3 . Next issue: sim_journal-of-automated-reasoning_1998_20_contents .