Rule Based Fuzzy Inference

We present a family of dialectic proof procedures for the admissibility semantics of assumption-based argumentation. These proof procedures are defined for any conventional logic formulated as a collection of inference rules and show how any such logic can be extended to a dialectic argumentation system. The proof procedures find a set of assumptions, to defend a given belief, by starting from an initial set of assumptions that supports an argument for the belief and adding defending assumptions incrementally to counter-attack all attacks. The proof procedures share the same notion of winning strategy for a dispute and differ only in the search strategy they use for finding it. The novelty of our approach lies mainly in its use of backward reasoning to construct arguments and potential arguments, and the fact that the proponent and opponent can attack one another before an argument is completed. The definition of winning strategy can be implemented directly as a non-deterministic program, whose search strategy implements the search for defences.

P2P applications for searching and exchanging information over the Web have become increasingly popular. This has lead to a number of (usually thematically) focused communities, which allow efficient searching within such communities, and which use specific metadata sets to specify the resources stored within the P2P network. By concentrating on domain and application specific formats for metadata and query languages, however, current P2P networks appear to be fragmenting into non-interoperable niche markets. This contribution describes the open source project Edutella which builds upon metadata standards defined for the WWW and aims to provide an RDF-based metadata infrastructure for P2P applications, building on the recently announced JXTA Framework. We describe one basic service (query) and an Edutella application (annotation) within this network, both being built on a common query language exchange format, and specify the main architecture and APIs of the Edutella P2P network. The advantage of the WWW is that it constitutes a predominantly decentral paradigm storing information resources in hypertext like structures. Searching in the WWW, however, typically follows a client-server model, viz. browser vs. search engine , inheriting the corresponding benefits and pitfalls. To name some problems, search engines cover only a decreasing percentage of the information available on the Web and their content is often not up to date because of the time required for crawling of the Web.

P2P applications for searching and exchanging information over the Web have become increasingly popular. This has lead to a num-ber of (usually thematically) focused communities, which allow efficient searching within such communities, and which use ...

The unification algorithm has long been a target for program synthesis research, but a fully automatic derivation remains a research goal. In deductive program synthesis, computer programming is phrased as a task in theorem proving; a declarative specification is expressed in logical form and presented to an automatic theorem prover, and a program meeting the specification is extracted from the proof. The correctness of the program is supported by the proof, which also provides an explanation of how the program works. The proof is conducted in an appropriate axiomatic subject-domain theory, which defines the concepts in the specification and the constructs in the target programming language and provides the background knowledge necessary to connect them.

In this paper, we propose a new tableau-based model checking technique for verifying dialogue game protocols for agent communication. These protocols are defined using our social commitment-based framework for agent communication called Commitment and Argument Network (CAN). We use a variant of CTL* (ACTL*) for specifying these protocols and the properties to be verified. This logic extends CTL* by allowing formulae to constrain actions as well as states. The verification method is based on the translation of formulae into a variant of alternating tree automata called Alternating Büchi Tableau Automata (ABTA). We propose a set of tableau rules (inference rules) for specifying this translation procedure. Unlike the model checking algorithms proposed in the literature, the algorithm that we propose in this paper allows us not only to verify if the dialogue game protocol (the model) satisfies a given property, but also if this protocol respects the tableau rules-based decomposition of the action formulae. This algorithm is an on-the-fly efficient algorithm.

In the development of embedded software, modeling languages used within or across development phases e.g., requirements, specification, design, etc are based on different paradigms and an approach for relating these is needed. In this paper, we present a formal framework for relating specification and design models of embedded systems. We have chosen UML statemachines as specification models and ProCom component language for design models. While the specification is event-driven, the design is based on time triggering and data flow. To relate these abstractions, through the execution trajectories of corresponding models, formal semantics for both kinds of models and a set of inference rules are defined. The approach is applied on an autonomous truck case-study.

We propose a rule-based system built on top of the capabilities of Mathematica to program non-deterministic and partially defined computations. The system is called ρLog and has primitive operators for defining elementary rules and for computing with unions, compositions, reflexive-transitive closures, and normal forms of rule applications. Moreover, ρLog can compute proof objects, which are internal representations of deduction derivations which respect a specification given by the user. We describe the programming principles and constructs of ρLog, the structures used to encode deduction derivations, and the methods provided to manipulate and visualize them. ρLog is a renamed version of the rule-based programming system FunLog [9, 10]. We did this in order to avoid confusing it with FUNLOG [12], a system for functional programming and logic programming developed in the eighties. Like Elan [2], ρLog provides a suitable environment for specifying and implementing deduction systems in a language based on rewrite rules whose application is controlled by user-defined strategies. More precisely, ρLog allows to:

Abstract. We describe a matching algorithm for terms built over flexible arity function symbols and context, function, sequence, and individual variables. The algorithm is called a context sequence matching algorithm. Context variables allow matching to descend in term-trees ...

It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cognition could be described as information processing which infers rule-like laws from patterns in data-sets. Since information processing is the domain of computers, artificial cognitive systems are generally designed as pattern discoverers. This paper questions the validity of the information processing paradigm as an explanation for human cognition and a design principle for artificial cognitive systems. Firstly, it is known from the literature that people suffer from conditions such as information overload, superstition, and mental disorders. Secondly, cognitive limitations such as a small short-term memory, the seteffect, the illusion of explanatory depth, etc. raise doubts as to whether human information processing is able to cope with the enormous complexity of an infinitely rich (amorphous) world. It is suggested that, under normal conditions, humans construct information rather than process it. The constructed information contains anticipations which need to be met. This can be hardly called information processing, since patterns from the "outside" are not used to produce action but rather to either justify anticipations or restructure the cognitive apparatus. When it fails, cognition switches to pattern processing, which, given the amorphous nature of the experiential world, is a lost cause if these patterns and inferred rules do not lead to a (partial) reorganisation of internal structures such that constructed anticipations can be met again. In this scenario, superstition and mental disorders are the result of a profound and/or random restructuring of already existing cognitive components (e.g., action sequences). This means that whenever a genuinely cognitive system is exposed to pattern processing it may start to behave superstitiously. The closer we get to autonomous self-motivated artificial cognitive systems, the bigger the danger becomes of superstitious information processing machines that "blow up" rather than behave usefully and effectively. Therefore, to avoid superstition in cognitive systems they should be designed as information constructing entities.

In this report we describe a method to perform type inference over data stored in an RDBMS, where rules over the data are specified using OWL-DL. Since OWL-DL is an implementation of the Description Logic (DL) SHOIN (D), we are in effect implementing a method for SHOIN (D) reasoning in relational databases. Reasoning make be broken down into two processes of classification and type inference. Classification may be performed efficiently by a number of existing reasoners, and since classification alters the schema, it need only be performed once for any given relational schema as a preprocessor of the schema before creation of a database schema. However, type inference needs to be performed for each data value added to the database, and hence needs to be more tightly coupled with the database system. Previously, no technique has been proposed that implements SHOIN (D) type inference within an RDBMS. We propose such a technique, involving the use of triggers to perform reasoning over the data values as they inserted into the database. We demonstrate the soundness and performance of our approach by comparing an implementation of our technique against other existing approaches for less powerful reasoning over data in an RDBMS. The results show we provide the fastest query processing of any technique, despite having a more powerful reasoner.

This thesis answers the research question whether it is possible to use a first-order probabilistic causal logic as the event handling logic in a discrete event training simulation. Discrete event simulations are very useful for the training of staff in organisations. Such simulations require a great resemblance with the environment they simulate. The environment is dynamic, partially observable, probabilistic and goal or utility driven. Using first-order probabilistic causal logic as the event handling logic in a discrete event training simulation can contribute to that resemblance. Based on the OMG MDA framework, AORSL is a discrete event simulation language that uses agents in its modelling. ICL is a framework that is capable of modelling agents under uncertainty using logic programming, probability, game and decision theory. It is a high-level language that makes use of other embedded formalisms. CPL, a first-order probabilistic causal logic, is integrated and unified with ICL a...

This paper presents an inference algorithm that can discover temporal logic properties of a system from data. Our algorithm operates on finite time system trajectories that are labeled according to whether or not they demonstrate some desirable system properties (e.g. "the car successfully stops before hitting an obstruction"). A temporal logic formula that can discriminate between the desirable behaviors and the undesirable ones is constructed. The formulae also indicate possible causes for each set of behaviors (e.g. "If the speed of the car is greater than 15 m/s within 0.5s of brake application, the obstruction will be struck") which can be used to tune designs or to perform on-line monitoring to ensure the desired behavior. We introduce reactive parameter signal temporal logic (rPSTL), a fragment of parameter signal temporal logic (PSTL) that is expressive enough to capture causal, spatial, and temporal relationships in data. We define a partial order over the set of rPSTL formulae that is based on language inclusion. This order enables a directed search over this set, i.e. given a candidate rPSTL formula that does not adequately match the observed data, we can automatically construct a formula that will fit the data at least as well. Two case studies, one involving a cattle herding scenario and one involving a stochastic hybrid gene circuit model, are presented to illustrate our approach.

Web applications must deal with a rapidly evolving, distributed, heterogeneous environment of information and reasoning services. Many applications obtain information from multiple sources and thus need to determine how, why, and when to use answers. Currently few if any webbased reasoning services make explanations available and, if they do, the supporting proofs of these explanations are rarely portable across applications. Thus, proofs, if available, are difficult to understand and more difficult to combine. This ...

This paper describes an operational semantics for DATR theories. The semantics is presented as a set of inference rules that axiomatises the evaluation relationship for DATR expressions. The inference rules provide a clear picture of the way in which DATR works, and should lead to a better understanding of the mathematical and computational properties of the language.

We propose a method which allows us to develop tableaux modulo theories using the principles of superdeduction, among which the theory is used to enrich the deduction system with new deduction rules. This method is presented in the framework of the Zenon automated theorem prover, and is applied to the set theory of the B method. This allows us to provide another prover to Atelier B, which can be used to verify B proof rules in particular. We also propose some benchmarks, in which this prover is able to automatically verify a part of the rules coming from the database maintained by Siemens IC-MOL.

EU-IST Integrated Project (IP) IST-2003-506826 SEKT Deliverable D3.4.1.1 (WP3.4) This document is an informal deliverable provided to SEKT WP3 partners. In this document, a general framework for reasoning with inconsistent ontologies is proposed. An inconsistency reasoner is one which is able to return meaningful answers to queries, given an inconsistent ontology. The formal definitions of soundness, meaningfulness, local completeness, and maximal completeness of an inconsistency reasoner are introduced. A pre-processing algorithm and a strategy for inconsistency reasoning based on linear extensions and selection functions are investigated. This document also contains a chapter that discusses the design of reasoners with inconsistent ontologies (RIO). A RIO architecture, which is based on the DIG description logic interface, is proposed.

In this paper we show how personalization techniques from the area of adaptive hypermedia can be achieved in the semantic web. Our approach is based on rule-based reasoning enabled by semantic web technologies. The personalization techniques are formalized as reasoning rules. The rules are able to reason over distributed information resources annotated with semantic web metadata formats. This leads towards the realization of an adaptive semantic web idea which provides personalized, adaptive access to information, services, or other, distributed resources.

We present a method to infer segmented and full volumetric descriptions of objects from intensity images. We use three weakly calibrated images from closely spaced viewpoints as input. Deriving full volumetric descriptions requires the development of robust inference rules. The inference rules are based on local properties of generalized cylinders (GCs). We jirst detect groups in each image based on proximity, parallelism and symmetry. The groups in the three images are matched and their contours are labelled as "true" and "limb" edges. We use the information about groups and the label associated with their contours to recover visible surfaces and their surface axes. To extract the complete volume in terms of a GC, we need to infer the GC axis, its cross section and the scaling function. The properties of straight and curved axis generalized cylinders are used locally on the visible surfaces to obtain the GC axis. The cross section is recovered i f seen in the images, else it is inferred using the visible surfaces and GC properties. We consider groups with true edges, limb edges or a combination of both. The$nal descriptions are volumetric and in terms of parts. Sometimes, when not enough information is present to make volumetric inferences, the descriptions remain at the surface level. We demonstrate results on real images of moderately complex objects with texture and shadows.

We present a method to infer segmented and full volumetric descriptions of objects from intensity images. We use three weakly calibrated images from closely spaced viewpoints as input. Deriving full volumetric descriptions requires the development of robust inference rules. The inference rules are based on local properties of generalized cylinders (GCs). We jirst detect groups in each image based on proximity, parallelism and symmetry. The groups in the three images are matched and their contours are labelled as "true" and "limb" edges. We use the information about groups and the label associated with their contours to recover visible surfaces and their surface axes. To extract the complete volume in terms of a GC, we need to infer the GC axis, its cross section and the scaling function. The properties of straight and curved axis generalized cylinders are used locally on the visible surfaces to obtain the GC axis. The cross section is recovered i f seen in the images, else it is inferred using the visible surfaces and GC properties. We consider groups with true edges, limb edges or a combination of both. The$nal descriptions are volumetric and in terms of parts. Sometimes, when not enough information is present to make volumetric inferences, the descriptions remain at the surface level. We demonstrate results on real images of moderately complex objects with texture and shadows.

Privacy has been a major concern for agents in distributed constraint reasoning. In this work, we approach this issue in distributed constraint reasoning by letting agents compromise solution quality for preserving privacy, using utility theory. We formalize privacy in the context of distributed constraint reasoning, detail its different aspects, and present model and solvers, as well as their properties. We then show how distributed constraint reasoning with privacy requirements can be modelled as a planning problem, and more specifically as

We merge two popular optimization criteria of Distributed Constraint Optimization Problems (DCOPs)reward-based utility and privacy -into a single criterion. Privacy requirements on constraints has classically motivated an optimization criterion of minimizing the number of disclosed tuples, or maximizing the entropy about constraints. Common complete DCOP search techniques seek solutions minimizing the cost and maintaining some privacy. We start from the observation that for some problems we could provide as input a quantification of loss of privacy in terms of cost. We provide a formal way to integrate this new input parameter into the DCOP framework, discuss its implications and advantages.

Elimination of frictional losses has been receiving large attention to reduce power consumption and the maintenance cost thus increasing the power efficiency. Considering the above facts, Magnetic levitation technology has given the contribution in industry as a part. Now, since Maglev's are highly nonlinear and inherently unstable, the objective is to design a controller that would be an attractive alternative to the existing classical or modern controllers challenging Non-linear control systems without the requirement of any system model or complex mathematical equations governing the transfer function. These requirements raised the design and analysis of Fuzzy Logic Controllers (FLC). Furthermore, the investigation on Proportional Integrated Derivative Controller (PID) is also reported here and its output is compared with that of Fuzzy controller to control the ball levitation in the air. The proposed controllers for MAGLEV system is simulated by using MATLAB/SIMULINK and implemented on a laboratory-scale magnetic levitation system.

In this paper, we propose a genetics-based knowledge integration approach to integrate multiple rule sets into a central rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each knowledge input is translated and expressed as a rule set, and then encoded as a bit string. The combined bit strings form an initial knowledge population, which is then ready for integrating. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial knowledge inputs. Furthermore, a rule-refinement scheme is proposed to refine inference rules via interaction with the environment. Experiments on diagnosing brain tumors were carried out to compare the accuracy of a rule set generated by the proposed approach with that of initial rule sets derived from different groups of experts or induced by means of various machine learning techniques. Results show that the rule set derived using the proposed approach is much more accurate than each initial rule set on its own.

This paper defines the (first-order) conflict resolution calculus: an extension of the resolution calculus inspired by techniques used in modern SAT-solvers. The resolution inference is restricted to (first-order) unit-propagation and the calculus is extended with a mechanism for assuming decision literals and a new inference rule for clause learning, which is a first-order generalization of the propositional conflict-driven clause learning (CDCL) procedure. The calculus is sound (because it can be simulated by natural deduction) and refutationally complete (because it can simulate resolution), and these facts are proven in detail here.

We present a family of dialectic proof procedures for the admissibility semantics of assumption-based argumentation. These proof procedures are defined for any conventional logic formulated as a collection of inference rules and show how any such logic can be extended to a dialectic argumentation system. The proof procedures find a set of assumptions, to defend a given belief, by starting from an initial set of assumptions that supports an argument for the belief and adding defending assumptions incrementally to counter-attack all attacks. The proof procedures share the same notion of winning strategy for a dispute and differ only in the search strategy they use for finding it. The novelty of our approach lies mainly in its use of backward reasoning to construct arguments and potential arguments, and the fact that the proponent and opponent can attack one another before an argument is completed. The definition of winning strategy can be implemented directly as a non-deterministic program, whose search strategy implements the search for defences.

This article is a by–product of the technical and financial support that the United Nations Fund for Population Activities (UNFPA) gives to Angola. The country located in the Middle Africa is experiencing at current times huge growth of displaced people as a consequence of war during the last 25years by now. During the last years the number of displaced population has reached frighten figures as the Angolan government as well as some humanitarian organisations estimate they are about 4 million . They are focus of great concern for local national and international authorities since their life conditions become more and more miserable. Thus upon request of the Angolan government UNFPA developed a project between 1999 and 2000 aiming to evaluate life conditions of families and reproductive health of the displaced in areas with important flows and in process of intensification. The project named "Reproductive Health and Family Survey among the displaced population” (RHFS) that incl...

Device representation and reasoning with affective relations occupies a middle ground between classical model-based diagnosis and heuristic expert systems. A device is modeled by specifying a set of diagnostically motivated affective relations among its components. Reasoning is then performed by a set of inference rules that reason with the model to propagate symptoms through the components. Representation and reasoning with affective relations extends several benefits of classical model-based diagnosis-the model as a unifying framework for knowledge, methodical coverage of the domain, and diagnostic reasoning based on equipment design and causality-to a class of problems where classical model-based diagnosis cannot be applied because the required models cannot be reasonably obtained or represented. Our work evolved from our redesign of a heuristic expert system for monitoring long-distance telephone switching systems, and is applicable to highly complex self-checking systems.

We study the relationship between Concurrent Separation Logic (CSL) and the assume-guarantee (A-G) method (a.k.a. rely-guarantee method). We show in three steps that CSL can be treated as a specialization of the A-G method for well-synchronized concurrent programs. First, we present an A-G based program logic for a low-level language with built-in locking primitives. Then we extend the program logic with explicit separation of "private data" and "shared data", which provides better memory modularity. Finally, we show that CSL (adapted for the low-level language) can be viewed as a specialization of the extended A-G logic by enforcing the invariant that "shared resources are wellformed outside of critical regions". This work can also be viewed as a different approach (from Brookes') to proving the soundness of CSL: our CSL inference rules are proved as lemmas in the A-G based logic, whose soundness is established following the syntactic approach to proving soundness of type systems.

This paper describes ontological description of computational agents, their properties and abilities. The goal of the work is to allow for autonomous behavior and semi-automatic composition of agents within a multiagent system. The system has to be create foundation for the interchangeability of computational components, and emergence of new models. This paper focuses on ways of representing agents and systems in standard formalisms, such as description logics, OWL, and Jade.

This paper describes ontological description of computational agents, their properties and abilities. The goal of the work is to allow for autonomous behavior and semi-automatic composition of agents within a multiagent system. The system has to be create foundation for the interchangeability of computational components, and emergence of new models. This paper focuses on ways of representing agents and systems in standard formalisms, such as description logics, OWL, and Jade.

We live in a world submerged with more information than ever before. We express information or data mathematically and it is growing faster than ever. If the data is imperfect, out of context or otherwise contaminated, it can lead to decisions that could undermine the competitiveness of an enterprise or damage the personal lives of individuals. Therefore, knowledge representation plays an important role in dealing with many aspects of problem solving. In order to obtain complete information systems, we use a mathematical tool, namely, Rough Set Theory (RST), which was introduced by Pawlak in 1982 as a way to deal with data analysis based on approximation methods in information systems. RST is a novel approach to cope with imperfect data analysis as well. In essence, the theory extends the classical crisp set to rough set by defining lower and upper approximations for any subset of a nonempty finite universe. The theory presupposes that with every object of the universe some informat...

We introduce and study a generalization of the theory of abstract algebraic logic (AAL), that explores the use of many-sorted behavioral logic in the role traditionally played by unsorted equational logic. Our aim is to extend the range of applicability of the traditional theory of AAL towards providing a meaningful algebraic counterpart also to logics with a many-sorted language and possibly including non-truth-functional connectives, which are not algebraizable according to the traditional theory. Furthermore, we pave the way towards a robust theory of behavioral AAL, namely by providing a behavioral version of the Leibniz operator which allows us to generalize the traditional Leibniz hierarchy, as well as several well-known characterization results. A number of meaningful examples will be used to illustrate the novelties and advantages of the approach.

Schwichtenberg developed the program called "Proofs as Programs" by measuring the "complexity" of proofs as programs using the Curry-Howard correspondence. In particular, he precisely characterized the complexity of proofs in arithmetic using proof-theoretic tools. In this paper, we extend this result to parameter-free subsystems of Girard's System F having the strength of Π 1 1-CA0, determining the expected complexity of terms in them (instead of proofs). This abstract setting of System F provides a new perspective on Schwichtenberg's result. His original goal was to determine the complexity of programs, relying on the Curry-Howard correspondence. In contrast, our approach directly determines the complexity of terms in System F , which are essentially programs themselves. Another advantage of this approach should be that our approach for these fragments is more uniform than the original, owing to the abstract setting of System F.

Sliding Mode Controller (SMC) have been studied and summarized in this paper. Various advantages of sliding mode controller (SMC) includes robustness, ability to deal with non-linear, time varying and multi-input, multi-output systems. SMC can also be designed for the fast dynamic response and good stability over a wide range. In spite of this, one of the main hindrances in the implementation of the sliding mode controller (SMC) is the chattering. Chattering; most of the times is undesirable, as it excites the unmodeled high frequency dynamics of the system. In order to overcome these drawbacks, this paper contains a brief review of a few techniques in research used for chattering elimination in SMC. Implementations of a few chattering reduction techniques and their validation results over the basic SMC have been proved.

High-performance SAT solvers based on systematic search generally use either conflict driven clause learning (CDCL) or lookahead techniques to gain efficiency. Both styles of reasoning can gain from a preprocessing phase in which some form of deduction is used to simplify the problem. In this paper we undertake an empirical examination of the effects of several recently proposed preprocessors on both CDCL and lookahead-based SAT solvers. One finding is that the use of multiple preprocessors one after the other can be much more effective than using any one of them alone, but that the order in which they are applied is significant. We intend our results to be particularly useful to those implementing new preprocessors and solvers.

1. INTRODUCTIONDynamic business environment drives firms towards practicing different growth strategies in order to successfully position themselves on the market. Growth strategies are concerned with increasing the size and viability of the business over time with the final aim of building and sustaining their competitive market position. A successful growth strategy will allow firms to increase its customer base, market segments, geographical scope, and/or product lines, which should lead to revenue growth. Permanent growth enables them to build and sustain their competitive market position.In planning growth strategies, managers should be concerned with three key issues: (1) where do we allocate resources within our business in order to achieve growth, (2) what changes in business scope do we see as compatible with growth and overall strategic decision, and (3) how do we time our growth moves compared to competitors (Harrison and St. John, 2008)?This study points out that in orde...

NARS-Python is an open-source implementation of a Non-Axiomatic Reasoning System (NARS) programmed in Python. This paper describes the overall architecture, control mechanism, and functionalities of NARS-Python v0.3, as well as some ideas for future work.

Previously we have proposed a logic, called priority logic 18, 20], where a theory consists of a collection of logic programming-like inference rules (without default negation) and a priority constraint among them. We showed that nonmonotonic reasoning in general can be viewed as selecting monotonic inferences that satisfy the speci ed priority constraints. The goal of this paper is to investigate how semantics of disjunctive programs can be understood as selecting monotonic inferences. We focus on credulous semantics. We show that the stable semantics of a disjunctive program can be represented by priority logic. The highlight of this paper is an interesting new semantics for disjunctive programs, discoved during this study, which has an abductive interpretation in the sense of Eshghi and Kowalski, whose abductive procedure can be adopted to serve as a credulous proof procedure for the new semantics.

This paper proposes a formalism for nonmonotonic reasoning based on prioritized argumentation. We argue that nonmonotonic reasoning in general can be viewed as selecting monotonic inferences by a simple notion of priority among inference rules. More importantly, these types of constrained inferences can be speci ed in a knowledge representation language where a theory consists of a collection of rules of rst order formulas and a priority among these rules. We recast default reasoning as a form of prioritized argumentation, and illustrate how the parameterized formulation of priority may be used to allow various extensions and modi cations to default reasoning. We also show that it is possible, but more di cult, to express prioritized argumentation by default logic: even some particular forms of prioritized argumentation cannot be represented modularly by defaults under the same language.

We propose an infrastructure based on the Open64 compiler for analyzing, modeling and optimizing MPI and/or OpenMP applications. The framework consists of four main parts: a compiler, microbenchmarks, a user interface and a runtime library. The compiler generates the application signature containing a portable representation of the application structure that may influence program performance. Microbenchmarks are needed to capture the system profile, including MPI latency and OpenMP overhead. The user interface, based on Eclipse, is used to drive code transformation, such as OpenMP code generation. And lastly, our runtime library can be used to balance the MPI workload, thus reducing load imbalance. In this paper we show that our framework can analyze and model MPI and/or OpenMP applications. We also demonstrate that it can be used for program understanding of large scale complex applications.

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Free download. Nine recommendations on ResearchGate as of March 6, 2025. This brief and carefree paper explains a quick and easy way to formulate formal modal logics for aesthetics. Often it may be the only way. The paper is written on the level of the general philosophy reader. It is intended to help art theorists who are not modal logicians by showing an easy way to formalize their own theories, as well as those of others. The good news is that it is not that hard. It is easier than you might think! Roger Fry’s theory of art is the example used here. We will be formalizing his theory as a modal logic. To be sure, not all art theorists will be interested in this, or in philosophy. In fact, that seems to be most of them! Such art theorists will merely lack philosophical depth and logical perspicuity. And just as many artists do art for art’s sake, I do philosophy and logic for philosophy and logic’s sake. All that said, if there’s anything you don’t understand, just skip over it.

Program analysis and verification tools crucially depend on the ability to symbolically describe and reason about sets of program behaviors. Separation logic provides a promising foundation for dealing with heap manipulating programs, while the development of practical automated deduction/satisfiability checking tools for separation logic is a challenging problem. In this paper, we present an efficient, sound and complete automated theorem prover for checking validity of entailments between separation logic formulas with list segment predicates. Our theorem prover integrates separation logic inference rules that deal with list segments and a superposition calculus to deal with equality/aliasing between memory locations. The integration follows a modular combination approach that allows one to directly incorporate existing advanced techniques for first-order reasoning with equality, as well as account for additional theories, e.g., linear arithmetic, using extensions of superposition. An experimental evaluation of our entailment prover indicates speedups of several orders of magnitude with respect to the available state-of-the-art tools.

This analysis is to identify requirements for a Description Logic (DL) to reason about product descriptions and other information related to product development. The DL is intended to be used by software decision support systems to assist in verification of product satisfaction criteria. Decision support requires the capability to express assertions about products and assemble evidence to demonstrate that the assertions are satisfied. Satisfaction criteria involve processing large amounts of data and so present significant scale up challenges for software systems. The analysis indicates need for the expressiveness found in OWL-DL, as well as additional descriptive expressiveness and inference rules. Experience with large scale information systems indicate that reasoning capability can be scaled up to serve large user communities. The use of reasoning capability can have significant cost and savings impact on product development

In this paper, Information Logic of Galois Connections (ILGC) suited for approximate reasoning about knowledge is introduced. In addition to the three classical propositional logic axioms and the inference rule of modus ponens, ILGC contains only two auxiliary rules of inference mimicking the performance of Galois connections of lattice theory, and this makes ILGC comfortable to use due to the flip-flop property of the modal connectives. Kripke-style semantics based on information relations is defined for ILGC. It is also shown that ILGC is equivalent to the minimal tense logic K t , and decidability and completeness of ILGC follow from this observation. Additionally, relationship of ILGC to the so-called classical modal logics is studied. Namely, a certain composition of Galois connection mappings forms a lattice-theoretical interior operator, and this motivates us to axiomatize a logic of these compositions. It turns out that this logic satisfies the axioms of the non-normal logic EMT4. Hence, EMT4 can be viewed to be embedded in ILGC. EMT4 is complete with respect to the neighbourhood semantics. Here, we introduce an alternative semantics for EMT4. This is done by defining the so-called interior models, and completeness of EMT4 is proved with respect to the interior semantics.

Acta Universitatis Sapientiae, Film and Media Studies, 6 (2013) 65−90.

As a possible cross section of cinematic narratology and the theory of intermediality the following essay assesses some of the effects of foregrounding the single, intermedial image within film. Specific figurations of intermediality that are experienced via the consciousness of single images, or some kind of “imageness” (i.e. as “intermedial references”) are presented as well as the modes in which they can interact with the narrative structure of a film. This interaction ranges from a momentary interference with the narrative structure of a film to disrupting it altogether; it may consist in constituting the gravity centre of a plot, or even in becoming the “canvas” for a palimpsest of narrative modes and narratives.

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