constructing finite algebras with falcon

The generation of models and counterexamples is an important form of reasoning. In this paper, we give a formal account of a system, called FALCON, for constructing finite algebras from given equational axioms. The abstract algorithms, as well as some implementation details and sample applications, are presented. The generation of finite models is viewed as a constraint satisfaction problem, with ground instances of the axioms as constraints. One feature of the system is that it employs a very simple technique, called the least number heuristic, to eliminate isomorphic (partial) models, thus reducing the size of the search space. The correctness of the heuristic is proved. Some experimental data are given to show the performance and applications of the system.

语义Web中的自动推理和主动规则终止性分析

语义Web是现有Web的一个语义延伸，目标是使Web的应用具有一定的智能，能够进行更准确的信息搜索和一定的决策支持，帮助用户自动完成一些任务。语义Web的实现需要人工智能、数据库等领域的技术支持，需要对已有的技术进行改进和扩展，也需要对现有技术本身如ECA规则进行更深入的研究从而进一步将其扩展到语义Web。自动推理技术是语义Web实现的基础之一，而ECA规则可以为系统增加反应性行为能力，本文围绕自动推理和ECA规则，主要进行了如下研究：（1） 一阶逻辑推理技术对语义Web的推理支持。分析了语义Web语言需要的表达能力，对现有描述逻辑工具、一阶逻辑工具的能力进行了考察。现有的描述逻辑工具不足以处理应用所需要的更丰富的语言成分、任意的一阶逻辑规则等，而一阶逻辑自动定理证明工具和有限模型查找工具有分别都有较好的研究成果。把语义Web的推理问题化成可满足性判定问题后，本文给出了一阶逻辑定理证明器和有限模型查找器形成的并发过程进行可满足性判定的方法。这样既可以解决描述逻辑工具的不足，并可弥补定理证明器对可满足的公式推理的不完备性。（2） ECA规则(主动规则)的终止性分析。终止性是主动规则最重要的行为性质，不终止是由于主动规则的级联触发造成的，意味着定义错误。终止性检查通常是不可判定的。有效的静态分析方法是主动规则得以实际应用的保证。静态分析的关键是合适的数据库状态描述，本文给出用约束表示数据库状态的方法，通过模拟规则执行，使用约束求解进行条件计算和重复状态检查，判断主动规则集合是否终止。用约束精确表示了数据库状态，在规则处理中考虑了执行语义，从而得到更精确的终止性结论。

A Model for Deliberation, Action, and Introspection

This thesis investigates the problem of controlling or directing the reasoning and actions of a computer program. The basic approach explored is to view reasoning as a species of action, so that a program might apply its reasoning powers to the task of deciding what inferences to make as well as deciding what other actions to take. A design for the architecture of reasoning programs is proposed. This architecture involves self-consciousness, intentional actions, deliberate adaptations, and a form of decision-making based on dialectical argumentation. A program based on this architecture inspects itself, describes aspects of itself, and uses this self-reference and these self-descriptions in making decisions and taking actions. The program's mental life includes awareness of its own concepts, beliefs, desires, intentions, inferences, actions, and skills. All of these are represented by self-descriptions in a single sort of language, so that the program has access to all of these aspects of itself, and can reason about them in the same terms.

Truth Maintenance Systems for Problem Solving

The thesis developed here is that reasoning programs which take care to record the logical justifications for program beliefs can apply several powerful, but simple, domain-independent algorithms to (1) maintain the consistency of program beliefs, (2) realize substantial search efficiencies, and (3) automatically summarize explanations of program beliefs. These algorithms are the recorded justifications to maintain the consistency and well founded basis of the set of beliefs. The set of beliefs can be efficiently updated in an incremental manner when hypotheses are retracted and when new information is discovered. The recorded justifications also enable the pinpointing of exactly whose assumptions which support any particular belief. The ability to pinpoint the underlying assumptions is the basis for an extremely powerful domain-independent backtracking method. This method, called Dependency-Directed Backtracking, offers vastly improved performance over traditional backtracking algorithms.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

A Simplified Clausal Resolution Procedure for Propositional Linear-Time Temporal Logic

The clausal resolution method for propositional linear-time temporal logic is well known and provides the basis for a number of temporal provers. The method is based on an intuitive clausal form, called SNF, comprising three main clause types and a small number of resolution rules. In this paper, we show how the normal form can be radically simplified, and consequently, how a simplified clausal resolutioin method can be defined for this impoprtant variety of logics.

On the EA-style integrated processing of self-contained mathematical texts

A sound and complete first-order goal-oriented sequent-type calculus is developed with ``large-block'' inference rules. In particular, the calculus contains formal analogues of such natural proof-search techniques as handling definitions and applying auxiliary propositions.

Reducing the gap between mobile and intelligent software agents

This paper presents an approach to integrate an artificial intelligence (AI) technique, concretely rule-based processing, into mobile agents. In particular, it focuses on the aspects of designing and implementing an appropriate inference engine of small size to reduce migration costs. The main goal is combine two lines of agent research, First, the engineering oriented approach on mobile agent architectures, and, second, the AI related approach on inference engines driven by rules expressed in a restricted subset of first-order predicate logic (FOPL). In addition to size reduction, the main functions of this type of engine were isolated, generalized and implemented as dynamic components, making possible not only their migration with the agent, but also their dynamic migration and loading on demand. A set of classes for representing and exchanging knowledge between rule-based systems was also proposed.

Semantic-based middleware solutions to support context-aware service provisioning in pervasive environments

The dynamicity and heterogeneity that characterize pervasive environments raise new challenges in the design of mobile middleware. Pervasive environments are characterized by a significant degree of heterogeneity, variability, and dynamicity that conventional middleware solutions are not able to adequately manage. Originally designed for use in a relatively static context, such middleware systems tend to hide low-level details to provide applications with a transparent view on the underlying execution platform. In mobile environments, however, the context is extremely dynamic and cannot be managed by a priori assumptions. Novel middleware should therefore support mobile computing applications in the task of adapting their behavior to frequent changes in the execution context, that is, it should become context-aware. In particular, this thesis has identified the following key requirements for novel context-aware middleware that existing solutions do not fulfil yet. (i) Middleware solutions should support interoperability between possibly unknown entities by providing expressive representation models that allow to describe interacting entities, their operating conditions and the surrounding world, i.e., their context, according to an unambiguous semantics. (ii) Middleware solutions should support distributed applications in the task of reconfiguring and adapting their behavior/results to ongoing context changes. (iii) Context-aware middleware support should be deployed on heterogeneous devices under variable operating conditions, such as different user needs, application requirements, available connectivity and device computational capabilities, as well as changing environmental conditions. Our main claim is that the adoption of semantic metadata to represent context information and context-dependent adaptation strategies allows to build context-aware middleware suitable for all dynamically available portable devices. Semantic metadata provide powerful knowledge representation means to model even complex context information, and allow to perform automated reasoning to infer additional and/or more complex knowledge from available context data. In addition, we suggest that, by adopting proper configuration and deployment strategies, semantic support features can be provided to differentiated users and devices according to their specific needs and current context. This thesis has investigated novel design guidelines and implementation options for semantic-based context-aware middleware solutions targeted to pervasive environments. These guidelines have been applied to different application areas within pervasive computing that would particularly benefit from the exploitation of context. Common to all applications is the key role of context in enabling mobile users to personalize applications based on their needs and current situation. The main contributions of this thesis are (i) the definition of a metadata model to represent and reason about context, (ii) the definition of a model for the design and development of context-aware middleware based on semantic metadata, (iii) the design of three novel middleware architectures and the development of a prototypal implementation for each of these architectures, and (iv) the proposal of a viable approach to portability issues raised by the adoption of semantic support services in pervasive applications.

A Conceptual Model to Combine Creativity Techniques with Fuzzy Cognitive Maps for Enhanced Knowledge Management

This chapter introduces a conceptual model to combine creativity techniques with fuzzy cognitive maps (FCMs) and aims to support knowledge management methods by improving expert knowledge acquisition and aggregation. The aim of the conceptual model is to represent acquired knowledge in a manner that is as computer-understandable as possible with the intention of developing automated reasoning in the future as part of intelligent information systems. The formal represented knowledge thus may provide businesses with intelligent information integration. To this end, we introduce and evaluate various creativity techniques with a list of attributes to define the most suitable to combine with FCMs. This proposed combination enables enhanced knowledge management through the acquisition and representation of expert knowledge with FCMs. Our evaluation indicates that the creativity technique known as mind mapping is the most suitable technique in our set. Finally, a scenario from stakeholder management demonstrates the combination of mind mapping with FCMs as an integrated system.

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

Towards Automation in Digital Investigations : Seeking Efficiency in Digital Forensics in Mobile and Cloud Environments

Cybercrime and related malicious activity in our increasingly digital world has become more prevalent and sophisticated, evading traditional security mechanisms. Digital forensics has been proposed to help investigate, understand and eventually mitigate such attacks. The practice of digital forensics, however, is still fraught with various challenges. Some of the most prominent of these challenges include the increasing amounts of data and the diversity of digital evidence sources appearing in digital investigations. Mobile devices and cloud infrastructures are an interesting specimen, as they inherently exhibit these challenging circumstances and are becoming more prevalent in digital investigations today. Additionally they embody further characteristics such as large volumes of data from multiple sources, dynamic sharing of resources, limited individual device capabilities and the presence of sensitive data. These combined set of circumstances make digital investigations in mobile and cloud environments particularly challenging. This is not aided by the fact that digital forensics today still involves manual, time consuming tasks within the processes of identifying evidence, performing evidence acquisition and correlating multiple diverse sources of evidence in the analysis phase. Furthermore, industry standard tools developed are largely evidence-oriented, have limited support for evidence integration and only automate certain precursory tasks, such as indexing and text searching. In this study, efficiency, in the form of reducing the time and human labour effort expended, is sought after in digital investigations in highly networked environments through the automation of certain activities in the digital forensic process. To this end requirements are outlined and an architecture designed for an automated system that performs digital forensics in highly networked mobile and cloud environments. Part of the remote evidence acquisition activity of this architecture is built and tested on several mobile devices in terms of speed and reliability. A method for integrating multiple diverse evidence sources in an automated manner, supporting correlation and automated reasoning is developed and tested. Finally the proposed architecture is reviewed and enhancements proposed in order to further automate the architecture by introducing decentralization particularly within the storage and processing functionality. This decentralization also improves machine to machine communication supporting several digital investigation processes enabled by the architecture through harnessing the properties of various peer-to-peer overlays. Remote evidence acquisition helps to improve the efficiency (time and effort involved) in digital investigations by removing the need for proximity to the evidence. Experiments show that a single TCP connection client-server paradigm does not offer the required scalability and reliability for remote evidence acquisition and that a multi-TCP connection paradigm is required. The automated integration, correlation and reasoning on multiple diverse evidence sources demonstrated in the experiments improves speed and reduces the human effort needed in the analysis phase by removing the need for time-consuming manual correlation. Finally, informed by published scientific literature, the proposed enhancements for further decentralizing the Live Evidence Information Aggregator (LEIA) architecture offer a platform for increased machine-to-machine communication thereby enabling automation and reducing the need for manual human intervention.