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Logic Programming and fuzzy logic are active areas of research, and their scopes in terms of applications are growing fast. Fuzzy logic is a branch of many-valued logic based on the paradigm of inference under vagueness. In this work we recall some of the interplay between three 3-valued logics that are relevant in these areas: The Lukasiewicz logic, the intermediate logic G3 and the paraconsistent logic G 3 ′ , and we present a contribution to the area of answer sets that consists in extending a definition of stable model based on proof theory in logic G3, to a more general definition that can be based on any of the more expressive logics G 3 ′ or Lukasiewicz. Finally we present and explore a new 4-valued logic that bears relation to G3 and to Lukasiewicz 4-valued logic.

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing, among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. We present two strategies for parallel online learning with OLED . We evaluate our proposed approaches on three datasets from the domains of activity recognition and maritime surveillance and show that they can significantly reduce training times, while they are capable of achieving super-linear speed-ups under certain circumstances.

Logic gates using magnetic tunnel junction (MTJ)-based nonvolatile logic-in-memory (NV-LIM) architecture are designed for quantized neural networks (QNNs) for Internet-of-Things applications. The NV-LIM-based implementation reduces data transfer costs between storage and logic gate components, thereby greatly enhancing the energy efficiency of inference operations in QNNs. The impact of the proposed nonvolatile logic gates for binary and ternary neural networks on energy consumption, delay, and area overhead reduction is demonstrated through circuit evaluations based on the parameters of the measured MTJ devices.

In the style of Lindstr\"om's theorem for classical first-order logic, this article characterizes propositional bi-intuitionistic logic as the maximal (with respect to expressive power) abstract logic satisfying a certain form of compactness, the Tarski union property and preservation under bi-asimulations. Since bi-intuitionistic logic introduces new complexities in the intuitionistic setting by adding the analogue of a backwards looking modality, the present paper constitutes a non-trivial modification of previous work done by the authors for intuitionistic logic in: G. Badia and G. Olkhovikov. A Lindstr\"om theorem for intuitionistic propositional logic. Notre Dame Journal of Formal Logic, 61 (1): 11-30 (2020)., Comment: 27 pages, 0 figures. arXiv admin note: text overlap with arXiv:2103.17024

17 USC 105 interim-entered record; under temporary embargo. Logic regression (LR), not to be confused with logistic regression, is a well-known alternative tree-based method and powerful statistical learning technique that can be used to classify a binary response using Boolean combinations of binary predictors. In our case, given the binary states of the components of a network and its corresponding operating or failed status, LR can quantify the importance of the interactions of components according to their predictive capabilities (strength for classification). Meaning that, unlike traditional approaches in the reliability field, a completely different assumption is used. This paper shows the application of logic regression in six networks. Each example is characterized by a matrix representing the status of each component and a vector showing the corresponding network status. These data are analytically derived or using simulation procedures. The results show that LR could be considered as an additional assessment tool, where the most important effects (single or interactions) of components emerge naturally as a result of an optimization problem. As a byproduct, LR is also able to detect possible minimal cut/path sets. U.S. Government affiliation is unstated in article text.

Departing from basic concepts in abstract logics, this paper introduces two concepts: conjunctive and disjunctive limits. These notions are used to formalize levels of modal operators.

As the usage of theorem prover technology expands, so too does the reliance on correctness of the tools. Metamath Zero is a verification system that aims for simplicity of logic and implementation, without compromising on efficiency of verification. It is formally specified in its own language, and supports a number of translations to and from other proof languages. This paper describes the abstract logic of Metamath Zero, essentially a multi-sorted first order logic, as well as the binary proof format and the way in which it can ensure essentially linear time verification while still being concise and efficient at scale. Metamath Zero currently holds the record for fastest verification of the set.mm Metamath library of proofs in ZFC (including 71 of Wiedijk’s 100 formalization targets), at less than 200 ms. Ultimately, we intend to use it to verify the correctness of the implementation of the verifier down to binary executable, so it can be used as a root of trust for more complex proof systems.

The purpose of the current study was to form theoretical issues and practical recommendations on development of methodology evaluation for companies implementing modern financial technologies. The research was conducted based on methodology of systematic research, synthesis, abstract logic and scientific forecast, statistical methods and factor analysis methods, expert evaluation, real option method, explored fuzzy logic. Research resulted in discovery of such companies’ features that implement modern financial technology as evaluation objects, as well as exogenous and endogenous factors that impact the evaluation of the researched companies, and these factors’ classification offered; there were methodological recommendations developed on qualitative and quantitative ranking of specific indeterminacy; there were adaptations performed to discounted cash flows, real options, and ratio methods. The methodology developed was approbated based on domestic and foreign companies that provide consulting and evaluations services. As a result of its usage, a more precise evaluation was provided for the companies implementing modern financial technologies. A separate practical usage regarded the adopted method of discounting rate calculation based on the correction for specific fintech implementation risks and the developed mechanism of real options usage for additional company’s value that allows receiving a more accurate evaluation numbers. The classification manual of specific risks developed by the author is used by evaluation practitioners. The scientific issues and practical recommendations on improvement of the companies’ evaluation that were developed within the study are being used for teaching of “Evaluation and business value management” course in Financial University.

Logic reasoning, a fundamental ability of human intelligence, is an important topic in artificial intelligence. Often, these reasoning patterns need to beforehand be provided by some domain experts, which has resulted in an interesting research topic: can reasoning patterns be directly learned from given data in which meaning of any symbols is not known in advance? In order to study this issue, in this study, we design four visual logic data set (named Fashion-Logic) using the Fashion-MNIST data, in which first each image is embedded with n special figure symbols each of which is a picture from the Fashion-MNIST data set, then a relation (the relations: Bitwise And, Bitwise Or, Addition and Subtraction are used in this paper) is added to any two images to generate one output image. And these resulting images are called the logical images. Our aim is to obtain a learning model that can directly mine and reason the logic relation between two input images and one output image, without presetting any reasoning patterns (i.e. a learning model does not know the meaning of the figure symbols embedded in the images and the relation between the input images and the output image in advance). This novel task, mining the logic patterns from the Fashion-Logic data set, is called a fashion logic task in this paper. In this work, we test the performances of many typical neural network models on Fashion-Logic data set, and produce fruitful results which indicates that it is feasible to directly mine the logic reasoning patterns using a learning strategy from given data. Last, we test and find that the ability of models is very poor to mine the abstract logic patterns on the more complex data sets called Fashion-LogicV2.

Logic locking/obfuscation has emerged as an auspicious solution for protecting the semiconductor intellectual property (IP) from the untrusted entities in the design and fabrication process. Logic locking disguises the implementation and functionality of the IP by implanting additional key-gates in the circuit. The right output of the locked chip is produced, once the correct key value is available at the input of the key-gates. The confidentiality of the key is imperative for the security of the locked IP as it stands as the lone barrier against IP infringement. Therefore, the logic locking is considered as a broken scheme once the key value is exposed. The logic locking techniques have shown vulnerability to different classes of attacks, such as Oracle-guided and physical attacks. Although the research community has proposed a number of countermeasures against such attacks, none of them is simultaneously unbreakable against Oracle-guided, Oracle-less, and physical attacks. Under such circumstances, a defense-in-depth mechanism can be considered as a feasible approach in addressing the vulnerabilities of logic locking. Defense-in-depth is a multilayer defense strategy where several independent countermeasures are implemented in the device to provide aggregated protection against different attack vectors. Introducing such a multilayer shielding model in logic locking is the major contribution of this paper. With regard to this, we first identify the core components of logic locking schemes, which need to be protected. Afterwards, we categorize the vulnerabilities of core components according to potential threats for the locking key in logic locking schemes. Furthermore, we propose several defense layers and countermeasures to protect the device from those vulnerabilities. In conclusion, we believe that a logic locking technique with a layered defense mechanism can be a possible solution against IP piracy.

When obtaining a full-fledged model for diagnostic and debugging purposes is out of reach, abstract logic models might allow us to fall back to abductive reasoning for isolating faults. Such models often only aggregate knowledge about which inputs and faults would have this or that effect on the system. Like in property-based system design or formal verification, we have that the quality of the resulting reasoning process depends heavily on this logic model. Since logic descriptions are not entirely intuitive to formulate and automated processes to derive them are prone to be incomplete, we'd certainly be interested in assessing a model's quality and isolate issues. In this paper, we're proposing to use test cases and spectrum-based fault localization for this task, drawing on the flexibility and ease-of-use of such a spectrum-based concept. Focusing on logic models formulated in propositional Horn-clauses, we provide examples that show the attractiveness of our concept.

Smart products, adaptive designs, and intelligent spaces are in the forefront of current artistic discourse. Regardless of oneÂ?s field - design or science - interaction design projects often benefit from efficient production methods for prototypes for beginners. This paper presents an educational case study and its pedagogical lessons from a project-based course for beginning design students to produce interactive prototypes using sensors, actuators, and microcontrollers. A series of short project-based modules using scaffolding of code templates in conjunction with toolkits for physical prototypes were introduced in order to learn fundamental technical knowledge and skills in the first half; then more open-ended investigation of project-based individual creative final projects followed. Each module can be completed in one day with instructions on prototyping and programming in pairs, allowing students to build and see abstract logic in programming through the physical behaviors of prototypes without overpowering student creativity and motivation. Students can reinterpret given materials and modify them to produce custom tools that can realize their original project goals. This strategy allows students to acquire extensible knowledge that does not rely on higher-level software functions or specialized but inflexible plug-ins. This paper is an extended and revised version of a paper presented at the EUROGRAPHICS 2014 conference 30]. Graphical abstractDisplay Omitted HighlightsThe paper introduces interactive prototypes produced by design students.The project-based approach with practical learning modules yields creative results.Learning modules consist of digital fabrication and programming techniques in pairs.The proposed teaching approach can be applied to fields other than design.The approach provides students experience in interdisciplinary collaboration.

College algebra syllabus setting is of utmost importance for the higher education developments and students education and training, especially for the newly opened bachelor majors. In this research, we investigated the traditional syllabus of college algebra in a nationwide manner, and discussed the algebraic fundamentals in artificial intelligence. Three case studies were proposed to address the fundamentals of abstract logic thinking, data observation and knowledge inference, and data-driven thinking. Eventually, a suggested syllabus for “Advanced Algebra and its Applications in AI” was introduced subsequently for the new major of “artificial intelligence”.

In Computer Science stepwise refinement of algebraic specifications is a well-known formal methodology for rigorous program development. This paper illustrates how techniques from Algebraic Logic, in particular that of interpretation, understood as a multifunction that preserves and reflects logical consequence, capture a number of relevant transformations in the context of software design, reuse, and adaptation, difficult to deal with in classical approaches. Examples include data encapsulation and the decomposition of operations into atomic transactions. But if interpretations open such a new research avenue in program refinement, (conceptual) tools are needed to reason about them. In this line, the paper’s main contribution is a study of the correspondence between logical interpretations and morphisms of a particular kind of coalgebras. This opens way to the use of coalgebraic constructions, such as simulation and bisimulation, in the study of interpretations between (abstract) logics., Fundação para a Ciência e a Tecnologia (FCT)

Mathematical concepts are usually abstract and logic-related. Pupils need to master a lot of mathematical concepts to smoothly solve mathematical problems and conduct mathematical reasoning and judgments. However, at the transition stage from specific image thinking to abstract logic thinking, pupils have difficulties to understand abstract mathematical concepts. Based on learning activity theory, this paper proposed five activities of visualization learning, namely, situation, thinking, interaction, lucubration and evaluation. It aims to cultivate the pupil’s abilities on independent learning, mathematical thought, cooperative exploration, acquiring and restructuring of knowledge, and proposing and solving problems. Through two rounds of action researches, these were applied to learning geometrical concepts of elementary mathematics. The evaluation results showed that the visualization learning of elementary mathematics can improve learning abilities.

Данная статья посвящена проблеме оснований логики. Прослеживается путь развития логики к обобщению и абстракции. Центральными понятиями здесь являются минимальная логика, базовая логика, абстрактная логика, универсальная логика, протологика.

Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analysis. They are commonly used by assigning weights to models on a purely descriptive basis (nominal scale). This invites the creation of unintended inconsistencies regarding the weights on the corresponding hazard curves. On the other hand, for human experts it is difficult to confidently express degrees-of-beliefs in particular numerical values. Here we demonstrate for ground-motion models how the model and the value-based perspectives can be partially reconciled by using high-dimensional information-visualization techniques. For this purpose we use Sammon's (1969) mapping and self-organizing mapping to project ground-motion models onto a two-dimensional map (an ordered metric set). Here they can be evaluated jointly according to their proximity in predicting similar ground motions, potentially making the assignment of logic tree weights consistent with their ground motion characteristics without having to abandon the model-based perspective.

Logic trees have become a standard feature of probabilistic seismic hazard analyses (PSHA) for determining design ground motions. A logic tree's purpose is to capture and quantify the epistemic uncertainty associated with the inputs to PSHA and thus enable estimation of the resulting uncertainty in the hazard. There are many potential pitfalls in setting up a logic tree for PSHA, mainly related to the fact that in practice, it is questionable that the requirements that the logic-tree branches be both mutually exclusive and collectively exhaustive can actually be met. Careful consideration is also required for making use of the output; in particular, in view of how PSHA is employed in current engineering design practice, it may be more rational to determine the mean ground motion at the selected design return period rather than to find the ground motion at the mean value of this return period.

Component based development is gaining increasing popularity for agile system development through reuse of existing asset. To make this approach effective enough to implement, the paradigm of "solution = components + composition logic" should be successfully realized. The current component composition approaches are either not flexible enough, have less visibility of the composition logic or demanding heavy platform and technology dependencies. Abstract Logic Tree is a mechanism of using a task decomposition approach based on a divide-and-conquer strategy to support composition design and execution. In this paper, the design of the Abstract Logic Tree and related methodology, the composition logic modeling capability, and the supporting framework named DynaTree are presented. The travel plan case is used to demonstrate the solution development process through the Abstract Logic Tree method.

Reliability has emerged as an important design criterion due to shrinking device dimensions. To address this challenge, researchers have proposed techniques compromising the Quality-of-Service across all design abstractions. Performing cross-layer reliability-QoS trade-off is a major challenge, which requires strong understanding of the fault propagation through different design abstractions. In this paper, we propose an analytical error prediction framework, based on probabilistic error masking matrices. The prediction is performed by propagating erroneous tokens through abstract logic networks. We report detailed experiments using a RISC processor and several embedded applications. The proposed approach demonstrates significantly faster reliability evaluation compared to pure simulation-driven approach, while predicts the erroneous effects of injected faults in both architecture and application levels. Several novel techniques are also proposed to increase the accuracy of error prediction.

Nonmonotonic Multi-Context Systems MCS provide a rigorous framework and flexible approach to represent and reason over interlinked, heterogeneous knowledge sources. Not least due to nonmonotonicity, however, an MCS may be inconsistent and resolving inconsistency is a major issue. Notions of diagnosis and inconsistency explanations have been developed for this purpose, considering the information exchange as the primary culprit. To discriminate between different possible solutions, we consider preference-based diagnosis selection. We develop a general meta-reasoning technique, i.e., an MCS transformation capable of full introspection on possible diagnoses, and we present a natural encoding of preferred diagnosis selection on top. Moreover, for the more involved notions of diagnosis utilized, we establish that the complexity does not increase. However, this does not carry over to selecting most preferred diagnoses as the encoding is not polynomial.

Logic paradigms are deeply rooted underlying assumptions that are used to frame thinking, decisions, and processes. We compare the fundamental assumptions of two logic paradigms—formal logic and paradoxical logic—and discuss the implications of these assumptions for innovation and change. Using Intel as an example, we suggest that paradoxical logic can enhance the innovative processes of high-technology firms, can help these firms to deal effectively with change, and, when combined with formal logic, can enhance firm performance. We derive three propositions from our analysis of Intel to guide future research.

Logic Controllers are often used to control continuous or hybrid processes. In these cases, the connection to the plant is realized by implelnenting pre- and post-processors for the conversion between analog and binary data. Hence, the controller remains purely discrete. However, the pre- and post-processors can not be included in the controller analysis. To overcome this problenl, Signal Interpreted Petri Nets, a Petri net model for the specification of PLC programs, are extended to include nonbinary signals. In this contribution hierarchical SIPN are presented. The analysis of the hierarchical net is based on the analysis of its subnets.

Logic synthesis of digital circuits is recognised as very important part of design process. There exist many different methods designed to achieve different goals. One of this methods is general functional decomposition. Though it is mainly perceived as a method used for implementation of Boolean functions into FPGA-based architectures, it has found applications in many other fields of modern engineering and science. In this paper, an application of functional decomposition in different fields of digital designing is presented, particularly a method of FSMs implementation using flip-flops or ROM memory. The experimental results prove that functional decomposition can help implementing circuits in FPGA, CPLD, the newest FPGA architectures with embedded memory blocks and in VLSI technology.

The management of inconsistent knowledge bases has been intensely studied in AI for a long time now; however, most of the proposals provide an a priori conflict resolution mechanism. A user who uses a system based on this past work is forced to use the semantics implemented in the system. In this work we propose a unified framework for reasoning about inconsistency in monotonic logics that captures existing approaches as a special cases. The main goal of our proposal is to provide the end-users with a formalism that allows them to bring their domain knowledge to bear in the process of dealing with inconsistency. In this chapter, we motivate the need for such a general framework by analyzing examples of inconsistent theories using different logics. Finally, we provide an introduction to the basic concepts that we use to define our framework, in particular a brief presentation of Tarski’s abstract logic.

Logic paths play an important role during the testing of an expert system, as the number of test cases to be used is determined by these paths, and they are used for testing the system under consideration during implementation. This paper proposes an algorithm based on a graph-theoretic clustering concept to determine the logic paths in an expert system. It also allows the testing of the expert system during the validation and verification phases for defining the optimal number of test cases. The proposed algorithm is efficient and compact, and guarantees the consistency of the underlying system during its implementation phase.

non-peer-reviewed Building intelligent robotic systems is both stirring and extremely challenging. Researchers have realized that robot intelligence can be achieved with a logical approach, but still AI struggles to connect that abstract logic with real-world meanings. This paper presents KnowLang, a new formal language for knowledge representation in a special class of intelligent robotic systems termed ASCENS. Autonomic Service-Component Ensembles (ASCENS) are multi-agent systems formed as mobile, intelligent and open-ended swarms of special autonomic service components capable of local and distributed reasoning. Such components encapsulate rules, constraints and mechanisms for self-adaptation and acquire and process knowledge about themselves, other service components and their environment. In this paper, a brief KnowLang case study of knowledge representation for a robotic system is presented.

The purpose of this paper is twofold: (1) to clarify Ludwig Wittgenstein’s thesis that colours possess logical structures, focusing on his ‘puzzle proposition’ that “there can be a bluish green but not a reddish green”, (2) to compare model-theoretical and game-theoretical approaches to the colour exclusion problem. What is gained, then, is a new game-theoretical framework for the logic of ‘forbidden’ (e.g., reddish green and bluish yellow) colours. My larger aim is to discuss phenomenological principles of the demarcation of the bounds of logic as formal ontology of abstract objects.

Logic synthesis can be achieved from the majority of VHDL, but some constructs have to be restricted. The techniques used in the TransGATE system to synthesize VHDL are explained. These ideas will be useful for those who wish to understand the process of designing hardware from VHDL models. It should be noted that the semantic completeness of VHDL extends only to constrain simulation tools and not to synthesis. Thus, techniques for hardware implementation described here will not necessarily be the same as other synthesis tools use. The main consideration for users of synthesis tools is to develop the design using a synchronous design methodology since there is no capability as yet for asynchronous circuit synthesis. With this restriction, synthesis can be used in the design of combinational logic processes, registered logic processes or finite state machines.

Logic programming with abductive reasoning is used during the realization of a data dictionary with a particular methodology. Some methodological steps are represented by means of a set ofrules augmented with integrity constraints which capture the presence of incompatible concepts. Explanations for incompatibility can be inferred by exploiting abductive reasoning . To this end a new proposal for the computation of hypotheses in an abductive framework where the theory is a general logic program, is presented. It is based on a suitable manipulation of minimal three-valued models of the logic program. A method to compute three-valued minimal models of a general logic program is given.

Logic programming and object-oriented programming have made significant contributions to the field of artificial intelligence, particularly as knowledge-based system development tools. Both these tools are extremely powerful and have several interesting features for application developers to exploit. The paper examines the potential benefits of integrating these two technologies to develop large knowledge-based applications, using each for what it does best. The salient features of the object-oriented and logic programming paradigms are summarized, and the advantages of combining them are discussed. A possible strategy is also provided for implementing the object-oriented paradigm within the logic-programming environment.

Developing programs faster and efficiently has been computer worker's pursuit goal for a long time. Procedure Blueprint is a visual process modeling language to develop programs conveniently, which makes up the imperfection of Unified Modeling Language in process description and can ensure the consistency of analysis, design and structure products in program development. What inadequate in the existing Procedure Blueprint is that concept, logic and implementation layer information need to manual input. Which not only exist many repeated manual labours, but also hinders the efficient program development seriously. This paper proposes a method, which is based on limited natural language process and module mapping, to realize the semi-auto conversion form logic layer to implementation layer of Procedure Blueprint. Applying this method to improve the existing Procedure Blueprint tool, on the one hand the tool keeps up the advantage of unifing the program development stages, on the other hand improves the efficiency and quality of program development.

LATIN aims at developing methods, techniques, and tools for interfacing logics and related formal systems. These systems are at the core of mathematics and computer science and are implemented in systems like (semi-)automated theorem provers, model checkers, computer algebra systems, constraint solvers, or concept classifiers. Unfortunately, these systems have differing domains of applications, foundational assumptions, and input languages, which makes them non-interoperable and difficult to compare and evaluate in practice.

Logic programming realizes the ideal of “computation is deduction,” but not when floating-point numbers are involved. In that respect logic programming languages are as careless as conventional computation: they ignore the fact that floating-point operations are only approximate and that it is not easy to tell how good the approximation is. It is our aim to extend the benefits of logic programming to computation involving floating-point arithmetic. Our starting points are the ideas of Cleary and the CHIP programming language. Cleary proposed a relational form of interval arithmetic that was incorporated in BNR Prolog in such a way that variables already bound can be bound again. In this way the usual logical interpretation of computation no longer holds. In this paper we develop a technique for narrowing intervals that we relate both to Cleary's work and to the constraint-satisfaction techniques of artificial intelligence. We then modify CHIP by allowing domains to be intervals of real numbers. To reduce arithmetic primitives with interval domains, we use our interval narrowing technique as an implementation of the looking-ahead inference rule. We show that the result is a system where answers are logical consequences of a declarative logic program, even when floating-point computations have been used. We believe ours is the first system with this property.

Logic simulators usually provide an event related output. In order to automate the generation of the tester programming this output has to be transformed to a cyclic timing description which are usually are required by testers. The transformation has to consider two restrictions according to most testers: the programming cannot be changed during the test run and the testers do not provide enough clocks to generate different timings for each signal. Two strategies to handle these restrictions has been introduced: the programming is to be fixed as late as possible and the grouping of signals with similar timings and wave forms. The main parts of the transformation has been discussed with respect to these strategies and a rough structure of a transformation program has been given.

In this paper we develop a topological approach to a theory of model-theoretical abstract logics. A model-theoretical abstract logic is given by a set of expressions (formulas), a class of interpretations (models) and a satisfaction relation between interpretations and expressions. Notions such as theory, consequence, etc. are derived in a natural way. We define topologies on the space of (prime) theories and on the space of models. Then structural properties of a logic are mirrored in the respective topological spaces and can be studied now by topological means. We introduce the notion of logic-homomorphism, a map between model-theoretical abstract logics that preserve structural (topological) properties. We study in detail conditions under which logic-homomorphisms determine continuous or/and open functions between the respective topological spaces. We define a logic-isomorphism as a bijective logic-homomorphism. Such a map forces a homeomorphism on the corresponding topological spaces.

This article presents a combination of different temporal specification and verification techniques for real time systems. We develop a semantic model that can be used as a model for both transition based formalisms like TLA and the temporal logics by Manna and Pnueli as well as interval based formalisms like the Duration Calculus. On this model a new temporal logic, TLD, is defined, which combines the strengths of both kinds of formalisms by including all the essential operators of all the considered logics, the meaning of the operators being preserved. Finally, a general framework for relating logics is established, and we prove that TLD is indeed an extension of both DC and TLA.

Full first order linear logic can be presented as an abstract logic programming language in Miller's system Forum, which yields a sensible operational interpretation in the 'proof search as computation' paradigm. However, Forum still has to deal with syntactic details that would normally be ignored by a reasonable operational semantics. In this respect, Forum improves on Gentzen systems for linear logic by restricting the language and the form of inference rules. We further improve on Forum by restricting the class of formulae allowed, in a system we call G-Forum, which is still equivalent to full first order linear logic. The only formulae allowed in G-Forum have the same shape as Forum sequents: the restriction does not diminish expressiveness and makes G-Forum amenable to proof theoretic analysis. G-Forum consists of two (big) inference rules, for which we show a cut elimination procedure. This does not need to appeal to finer detail in formulae and sequents than is provided by G-Forum, thus successfully testing the internal symmetries of our system., Author website at http://alessio.guglielmi.name/res/

logic programming is about designing logic programming languages via the proof theoretic notion of uniform provability. It allows the design of purely logical, very expressive logic programming languages, endowed with a rich meta theory. This tutorial intends to expose the main ideas of this discipline in the most direct and simple way.

The aim of this paper is to survey some work done recently or still in progress that applies generalized matrices (also called abstract logics by some) to the study of sentential logics. My main concern will be to emphasize the links between this line of research and other existing frameworks in Algebraic Logic, either well-established ones (such as the old theory of logical matrices and the younger theories of protoalgebraie logics, algebraizable logics and the associated hierarchy) or really new ones (such as the theory of algebraizability of Gentzen systems or the model theory of equality-free logic). I would like to convey the idea that the interaction between these neighbouring fields may be specially fruitful, as it seems to be one of the leading forces in the shaping of this emerging field called Abstract Algebraic Logic.

This paper is a first step in a larger enterprise. The ultimate aim of my enterprise is to uncover the logical structures, in a strict sense of the word “logic”, typically involved in scientific enterprise, not just in the justification of already obtained results but in the acquisition of new information.

Dale Miller describes a logic programming language as an abstract logic programming language plus a quantifier elimination method. We present a new abstract logic programming language for disjunctive logic programming, extension of the SLD system: the SLOU system and a new quantifier elimination method for disjunctive logic programming, result of the combination of Robinson resolution and case-analysis: the OU-resolution method.

Logic and/or probability manipulations are only one aspect of a fault tree analysis (FTA) which should be viewed from a broader perspective including both risk assessment, risk management and system modeling. This chapter consists of two parts. The first part describes safety goals, accident causation mechanisms and systematic risk management methodologies to be reflected by FTA. The second part presents an automated synthesis algorithm because the synthesis is considered as the bottleneck of FTA.

Logic interpretations of brain-like large scale architectures are presented here. Neural networks with classifiers (symbolic NNs: SNNs) compute in steps with discrete symbols. Knowledge is stored and processed in localized NNs. Variables are always bound into a restricted domain, i.e., in a category. Each type of rule is stored in the NN group having only its input and output categories. General purpose rules are not needed. Inferences are fuzzy. Deductions are replaced by decisions when halting must be prevented. Logic elements are events and not permanent things. The logical interpreter is wired in the network. Processing in well-formed SNNs is best described by the natural expression: “reasoning by analogy”.

Logic programming has become extremely fashionable in recent years following its adoption as the basis of the Japanese Fifth Generation of Computer Systems and its increased prominence in national and international research and development programmes. Numerous implementations of PROLOG are in current commercial use, in the hands of inexperienced users with diverse backgrounds. This paper assesses the achievements, potential and particularly limitations of logic programming in system development.

Logic simulation is the process of exercising a realistic model of a digital system with sets of input stimuli. Computation time required for logic simulation is growing due to recent increases in the size, complexity and test requirements of the systems to be simulated. Hence, this has become one of the most important subjects in the area of computer-aided design. The objective of this research was to develop and implement a new speed-up algorithm which decreases computing requirements for software simulation. A reduced time interval partitioned simulation algorithm is presented. This new algorithm is based on a time first evaluation algorithm and the reduction of time intervals. The new algorithm partitions the circuit into standard subcircuits and searches the time intervals where the output value of a primitive is determined. It only evaluates the events which are not including in those time intervals. This new simulation algorithm is shown to be faster and more efficient than conventional algorithms.

Logic plays an important role within expert systems of enabling inference and representing meaning. We analyse and compare several logics in terms of their topic-neutral items, emphasizing the importance of semantic, as well as syntactic, validity. Fuzzy logic and PRUF are assessed for mechanizability. It is concluded that the existing logics are limited in their applicability and that a much closer analysis of the semantics of logic is required, with respect to computational feasibility and power of meaning representation.

The objective of the present paper is to introduce a new approach to the study of the logical structure of scientific theories. We shall consider, in broad outline, a basic framework for handling theories and for analyzing a number of key metatheoretical concepts. The framework itself may be formally captured within a suitable system of set theory. However, its major innovative and heuristic thrust derives from some notions em ployed in modern abstract logic; in particular, it encourages the application of model-theoretic concepts and results to questions con cerning the structure and dynamics of empirical theories. The viewpoint adopted here shares some features in common with previous attempts at framing metascientific concepts, especially with the 'logistic' or model-theoretic, and with the so-called structuralist ap proaches. Like the former, it stresses the use of formal semantics in metascience; with the latter, it also appeals to some of the more abstract and structural characteristics of theories. However, it differs from these and other traditional approaches, notably in the role in which logic is portrayed. The nature and function of logic in scientific theorizing is here construed in an unusually liberal fashion: it is neither seen as a constraint on the syntactical form of theories, nor viewed as imposing limitations on the kinds of inferences permitted in science. This flexible and versatile conception of logic will be seen to amount to a somewhat radical departure from traditional usage. It also constitutes what the authors believe to be the clearest grounds for upholding the present framework. It should be mentioned at the outset that no new formal results are brought in this paper;1 nor do we pretend to offer a complete and polished semantical machinery whose fine details are fully developed at each point. Instead, we shall attempt to present something of the flavour of this framework with a minimum of technical fuss.2 We shall also try to provide some arguments in its support, and to indicate how it may lead to some fresh insights on a range of familiar, and sometimes controversial

Logic programmings application in knowledge based systems require heavy use of images for effective human inter faces, and it needs powerful methods for describing, generating images. PROLOG as an AI language gives the user the possibility to describe concepts - in this way images - at very high level and can solve problems given declaratively as well. The high need of computer graphies in logic programming languages and the lack of it in present PROLOG implementations motivated us to elaborate a graphic system built into MPROLOG language and to analyze its role in AI application. A 3-dimensional graphic-called eangle graphic-based on turetle graphic was created . An implementiton is briefly descibed