Bridge Rules Research Articles

Research on Prediction Model of Fracture Width in Loss Formation Based on Artificial Neural Network

Lost circulation in fractured formation has always been a worldwide technical problem in oil and gas drilling projects at home and abroad. The effect of bridging plugging depend on the matching of the fracture width and the particle size of lost circulation material. The plugging formula can be optimized according to the bridge rules to improve the success rate of plugging. In this paper, a prediction model of fracture width based on artificial neural network was established. Input layer, hidden layer and output layer were included. 17 input parameters such as well depth, formation, density of drilling fluid and displacement were determined. The fracture width was the output parameter. By optimizing the number of hidden layers and their nodes, the optimized training set had 2 hidden layers, and the number of nodes in the first hidden layer was 4, and the number of nodes in the second hidden layer was 10, the prediction accuracy of the model was the best, with the RMSE of 1.24 and the maximum R2 of 0.94. The model runs well in the test set with RMSE of 1.28 and R2 of 0.92. The fracture width prediction results of 5 Wells showed that the prediction results of this model match well with the field measurement results, and the average relative error percentage is only 2.94%. The established model had high accuracy, which can provide a basis for early and accurate prediction of well loss risk on site and for guiding optimization of engineering parameters to prevent well loss.

A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems.

In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.

Self‐adaptation and distributed knowledge‐based service ecosystem evolution

SummaryWeb services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self‐adaptation technologies for the service‐based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self‐adaptation and distributed knowledge‐based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self‐adaptation mechanism. We adopt the “survival of the fittest” principle for crucial elements in the ecosystem during evolution so that ultimately, service‐based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service‐based process are automatically repaired.

Contextual and Possibilistic Reasoning for Coalition Formation

In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.

Intrinsic approaches to prioritizing diagnoses in multi-context systems

Multi-context systems introduced by Brewka and Eiter provide a promising framework for interlinking heterogeneous and autonomous knowledge sources. The notion of diagnosis has been proposed for analyzing inconsistency in multi-context systems, which captures a pair of subsets of bridge rules of a multi-context system needed to be deactivated and activated unconditionally, respectively, in order to restore the consistency for that system. Generally, diagnoses need to be prioritized from some specific perspectives in order to select the most desirable ones to resolve inconsistency. In this paper, we propose a series of intrinsic approaches to prioritizing diagnoses based on the structure of information exchange over contexts in a multi-context system, which allow us to rank diagnoses in cases where no external knowledge is available. We use a heterogeneous graph, termed information exchange network, to formulate the information exchange over contexts via bridge rules in a multi-context system. Then we propose three kinds of approaches to prioritizing diagnoses based on the information exchange network for a multi-context system. Approaches of the first kind focus on ranking diagnoses by evaluating the impact of potential revision according to each diagnosis on direct information exchange over contexts from different perspectives, whilst approaches of the second kind rank diagnoses by evaluating the impact of potential revision according to each diagnosis on betweenness centralities of contexts with regard to the whole information exchange network. The last one uses the betweenness centralities of bridge rules in the information exchange network to prioritize diagnoses directly.

Multi-Context System for Optimization Problems

This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional costassignment bridge rules to Multi-context Systems (MCS). This novel feature facilitates the definition of a preorder among equilibria, based on the total incurred cost of applied bridge rules. As an application of MCS-OP, the paper describes how MCS-OP can be used in modeling Distributed Constraint Optimization Problems (DCOP), a prominent class of distributed optimization problems that is frequently employed in multi-agent system (MAS) research. The paper shows, by means of an example, that MCS-OP is more expressive than DCOP, and hence, could potentially be useful in modeling distributed optimization problems which cannot be easily dealt with using DCOPs. It also contains a complexity analysis of MCS-OP.

Multi-context systems in dynamic environments

Multi-Context Systems (MCS) model in Computational Logic distributed systems composed of heterogeneous sources, or "contexts", interacting via special rules called "bridge rules". In this paper, we consider how to enhance flexibility and generality in bridge-rules definition and application. In particular, we introduce and discuss some formal extensions of MCSs useful for a practical use in dynamic environments, and we try to provide guidelines for implementations

Sigon: A multi-context system framework for intelligent agents

The usage of agents has become one of the most effective ways to deal with complex systems. The agent’s approach allows for the parallel execution of actions and eases the conception of the system. In this paper, we introduce a language, called Sigon, that allows the definition of agents as multi-context systems. Multi-context systems, in turn, allow for a modular description of the agent, and the implementation of bridge rules contributes to the system flexibility. We propose a generic framework to conceive agents, in which the system’s developer defines their internals in terms of contexts and bridge rules. In order to show the way in which the programmer can aggregate other logical contexts, we first present the configurations of a BDI-like agent in our framework. Subsequently, the programmer can add more contexts, such as emotional or negotiating ones, and develop other agent architectures. Thus, our approach can be characterized as a form of generic and extensible architecture. In order to validate our proposal, we introduce some examples that delineate some strengths of the proposal, and also we present an analysis of bridge rules execution to demonstrate the processing overhead.

Augmenting agent computational environments with quantitative reasoning modules and customisable bridge rules

There are many examples where large amounts of data might be potentially accessible to an agent, but the agent is constrained by the available budget since access to knowledge-bases is subject to fees. Also, there are several activities that an agent might plan and perform on the web where one or more stages imply the payment of fees. For instance, consider the issue of buying resources in a cloud computing context where the objective of the agent is to obtain the best possible configuration of a certain application within given budget constraints. In this paper, we consider the software-engineering problem of how to practically empower agents with the capability to perform budget-constrained reasoning in a uniform and principled way. To this aim, we enhance the ACE component-based agent architecture by means of a device for practical and computationally affordable quantitative reasoning, whose results actually determine one or more courses of agent's actions, also according to policies/preferences. We further enhance the ACE framework by making the agent-components interaction mechanism parametric with respect to the actual modules that an agent may dynamically decide to exploit. We discuss the proposed extensions on a realistic case-study.

Augmenting agent computational environments with quantitative reasoning modules and customisable bridge rules

Augmenting agent computational environments with quantitative reasoning modules and customisable bridge rules

The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes

BackgroundHigh-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically.ResultsWe present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rules on classes during automatic reasoning. Biological processes are thus automatically related to mathematical models. 37% of BiPON classes stem from different well-established bio-ontologies, while the others have been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression processes.ConclusionsBiPON is a proof of concept of the way to combine formally systems biology and bio-ontology. The knowledge formalization is highly flexible and generic. Most of the known cellular processes, new participants or new mathematical models could be inserted in BiPON. Altogether, BiPON opens up promising perspectives for knowledge integration and sharing and can be used by biologists, systems and computational biologists, and the emerging community of whole-cell modeling.

Preference-Based Inconsistency Management in Multi-Context Systems

Multi-Context Systems (MCS) are a powerful framework for interlinking possibly heterogeneous, autonomous knowledge bases, where information can be exchanged among knowledge bases by designated bridge rules with negation as failure. An acknowledged issue with MCS is inconsistency that arises due to the information exchange. To remedy this problem, inconsistency removal has been proposed in terms of repairs, which modify bridge rules based on suitable notions for diagnosis of inconsistency. In general, multiple diagnoses and repairs do exist; this leaves the user, who arguably may oversee the inconsistency removal, with the task of selecting some repair among all possible ones. To aid in this regard, we extend the MCS framework with preference information for diagnoses, such that undesired diagnoses are filtered out and diagnoses that are most preferred according to a preference ordering are selected. We consider preference information at a generic level and develop meta-reasoning techniques on diagnoses in MCS that can be exploited to reduce preference-based selection of diagnoses to computing ordinary subset-minimal diagnoses in an extended MCS. We describe two meta-reasoning encodings for preference orders: the first is conceptually simple but may incur an exponential blowup. The second is increasing only linearly in size and based on duplicating the original MCS. The latter requires nondeterministic guessing if a subset-minimal among all most preferred diagnoses should be computed. However, a complexity analysis of diagnoses shows that this is worst-case optimal, and that in general, preferred diagnoses have the same complexity as subset-minimal ordinary diagnoses. Furthermore, (subset-minimal) filtered diagnoses and (subset-minimal) ordinary diagnoses also have the same complexity.

Distributed First Order Logic

Distributed First Order Logic (DFOL) has been introduced more than ten years ago with the purpose of formalising distributed knowledge-based systems, where knowledge about heterogeneous domains is scattered into a set of interconnected modules. DFOL formalises the knowledge contained in each module by means of first-order theories, and the interconnections between modules by means of special inference rules called bridge rules. Despite their restricted form in the original DFOL formulation, bridge rules have influenced several works in the areas of heterogeneous knowledge integration, modular knowledge representation, and schema/ontology matching. This, in turn, has fostered extensions and modifications of the original DFOL that have never been systematically described and published. This paper tackles the lack of a comprehensive description of DFOL by providing a systematic account of a completely revised and extended version of the logic, together with a sound and complete axiomatisation of a general form of bridge rules based on Natural Deduction. The resulting DFOL framework is then proposed as a clear formal tool for the representation of and reasoning about distributed knowledge and bridge rules.

Design of a spatial database to analyze the forms and responsiveness of an urban environment using an ontological approach

This paper introduces a spatial database and ontology-enabled framework that models and operationalizes the relation between urban forms and their responsiveness to the needs of its user. The objective is to offer a framework that provides a practical implementation of the concept of responsive environment introduced by Bentley, Alcock, Murrain, McGlynn and Smith (1985) in order to provide enough reusability and flexibility to reflect different urban modeling viewpoints and conceptualizations. The developed system is organized in three modules: the first and second modules model the ontology of a responsive environment and the layout of an urban environment while the third module is a spatial database that supports further computational analysis of urban forms. We applied several spatial data metrics that analyze the structural properties of urban forms and the emerging opportunities as identified by the notion of responsive environments. The framework is experimented and applied to the district 12, region 4 of the city of Tehran in Iran. The modeling abstractions lead to the generation of several semantic bridge rules and map layers that reflect different levels of urban responsiveness. The emerging patterns are qualitatively evaluated and found to be correlated with inhabitants' own perception of an urban environment. The results show that the suggested framework can be applied to the analysis of the responsive environments of urban forms.

Elimination of Level Crossing using Limited Use Subway

This paper is about the elimination of a level crossing at Sellur, Madurai using a Limited Use Subway (LUS). This LUS is necessary for efficient transportation of people and goods within Madurai city. The total work involves mainly temporary arrangements to run the rail traffic, construction of the box culvert, removal of temporary arrangements and opening the LUS to public. The temporary arrangements include plate girders, built-up columns, bearing plates, slab bases and footings. These are required to avoid blockage of rail traffic when the subway is under construction. The elements of temporary arrangements are analysed by taking their dead loads and live loads as per Indian Railway Standards. They are designed manually as per the codal provisions specified in IS: 800 -1984. The members are checked for their load carrying capacity and other suitable factors. They are designed with a dynamic augment of 15% and hence they have to be frequently maintained (daily) and the rigidity of connections should be ensured. The speed of the trains over these arrangements is restricted to 20kmph. The LUS section adopted is RCC Box Culvert. The analysis of the culvert is done by considering various possible load cases involving super imposed dead loads (SDL), live loads, surcharge loads and its self-weight. The loads are taken as per the IRS Bridge Rules and the culvert is analysed for these loads using STAAD. Pro 2004. Using the analysis results, the culvert is designed manually using the formulae mentioned in IRS Sub-Structure Code. The sections at different points are arrived for top slab, bottom slab and outer walls. Finally the estimation and cost abstract for the project is prepared and the total cost is determined.

Distributed Evaluation of Nonmonotonic Multi-context Systems

Multi-context Systems (MCSs) are a formalism for systems consisting of knowledge bases (possibly heterogeneous and non-monotonic) that are interlinked via bridge rules, where the global system semantics emerges from the local semantics of the knowledge bases (also called “contexts”) in an equilibrium. While MCSs and related formalisms are inherently targeted for distributed set- tings, no truly distributed algorithms for their evaluation were available. We address this short- coming and present a suite of such algorithms which includes a basic algorithm DMCS, an ad- vanced version DMCSOPT that exploits topology-based optimizations, and a streaming algorithm DMCS-STREAMING that computes equilibria in packages of bounded size. The algorithms be- have quite differently in several respects, as experienced in thorough experimental evaluation of a system prototype. From the experimental results, we derive a guideline for choosing the appropriate algorithm and running mode in particular situations, determined by the parameter settings.

Query answering over contextualized RDF/OWL knowledge with forall-existential bridge rules: Decidable finite extension classes

The proliferation of contextualized knowledge in the Semantic Web (SW) has led to the popularity of knowledge formats such as quads in the SW community. A quad is an extension of RDF triple with the contextual information of the triple. We in this pa

Finding explanations of inconsistency in multi-context systems

Interlinking knowledge sources to enable information exchange is basic means to build enriched knowledge-based systems, which gains importance with the spread of the Internet. Inconsistency, however, arises easily in such systems, which is not least due to their heterogeneity, but also due to their independent design. This makes developing methods for consistency management of such systems a pressing issue. An important aspect is that in many relevant cases, the information at individual sources may not be amenable to change in order to resolve inconsistency, like in case of autonomous management of the sources. We thus aim at analyzing inconsistency of a system by means of the interlinking of sources and changes thereof. More concretely, we consider the powerful framework of Multi-Context Systems, in which decentralized and heterogeneous system parts interact via (possibly nonmonotonic) bridge rules for information exchange. Nonmonotonicity and potential cyclic dependencies pose additional challenges that call for suitable methods of inconsistency analysis. We thus provide two approaches for explaining inconsistency, which both characterize inconsistency in terms of bridge rules, but in different ways: by pointing out rules which need to be altered for restoring consistency, and by finding combinations of rules which cause inconsistency. We show duality and modularity properties of these notions, give precise complexity characterizations, and provide algorithms for their computation, which have been implemented in a prototype, by means of so-called hex-programs. Our results provide a basis for inconsistency management in heterogeneous knowledge systems which, different from and orthogonal to other works, explicitly addresses the knowledge interlinks in order to restore consistency.

Combining Multiple Ontologies Using Ε-Connections

Ontology reusing has been extensively researched. Ontology connection is one of the ways which can reuse ontology that has been studied lately. Ε-connections and bridge rules are two ways of connecting ontologies. Based on these two methods, and using the ontology editor tool SWOOP, the link properties of the classes from different ontologies can be defined. Using these link properties, the multiple ontologies we are connecting can be reused effectively.

Analysis on Pre-Stressed Friction Loss in Bending Channel under Contact Pressure Non-Uniform Distribution Hypothesis

The pre-stressed friction loss formula in bending channel recommended by Bridge Rules was based on the uniform distribution hypothesis of contact pressure. From some conflicts in application of the formula, the non-uniform distribution law of the contact pressure was revealed. On the basis of elastic contact analysis, two types of non-uniform distribution hypothesis were proposed, including cosine and second-degree parabola distribution. Then the contact positive pressure expressions were derived respectively and the frictional resistance was calculated to compare with the calculated value under the uniform distribution. The results indicate that the friction resistance under any non-uniform distribution of positive pressure is less than under the uniform distribution. So the uniform distribution assumption of contact pressure does not cause Bridge Specifications formula underestimate friction loss. It is necessary to do further research on the applicability of Coulomb’s law under the contact conditions in the curved channel.