28 research outputs found

    Large-scale Reasoning with Nonmonotonic and Imperfect Knowledge Through Mass Parallelization

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    Due to the recent explosion of available data coming from the Web, sensor readings, social media, government authorities and scientific databases, both academia and industry have increased their interest in utilizing this knowledge. Processing huge amounts of data introduces several scientific and technological challenges, and creates new opportunities. Existing works on large-scale reasoning through mass parallelization (namely parallelization based on utilizing a large number of processing units) concentrated on monotonic reasoning, which can process only consistent datasets. The question arises whether and how mass parallelization can be applied to reasoning with huge amounts of imperfect (e.g. inconsistent, incomplete) information. Potential scenarios involving such imperfect data and knowledge include ontology evolution, ontology repair and smart city applications combining a variety of heterogeneous data sources. In this thesis, we overcome the limitations of monotonic reasoning, by studying several nonmonotonic logics that have the ability to handle imperfect knowledge, and it is shown that large-scale reasoning is indeed achievable for such complex knowledge structures. This work is mainly focused on adapting existing methods, thus ensuring that the proposed solutions are parallel and scalable. Initially, preliminaries and literature review are presented in order to introduce the reader to basic background and the state-of-the-art considering large-scale reasoning. Subsequently, each chapter presents an approach for large-scale reasoning over a given logic. Large-scale reasoning over defeasible logic is supported allowing conflict resolution by prioritizing the superiority among rules in the rule set. A solution for stratified semantics is presented where rules may contain both positive and negative subgoals, thus allowing reasoning over missing information in a given dataset. The approach for stratified semantics is generalized in order to fully support the well-founded semantics, where recursion through negation is allowed. Finally, conclusion includes observations from a preliminary investigation on a restricted form of answer set programming, a generic evaluation framework for large-scale reasoning, a discussion of the main findings of this work, and opportunities for future work

    A survey of large-scale reasoning on the Web of data

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    As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

    Defeasible RDFS via Rational Closure

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    In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a prominent approach. In recent years, RC has gained even more popularity in the context of Description Logics (DLs), the logic underpinning the semantic web standard ontology language OWL 2, whose main ingredients are classes and roles. In this work, we show how to integrate RC within the triple language RDFS, which together with OWL2 are the two major standard semantic web ontology languages. To do so, we start from ρdf\rho df, which is the logic behind RDFS, and then extend it to ρdf\rho df_\bot, allowing to state that two entities are incompatible. Eventually, we propose defeasible ρdf\rho df_\bot via a typical RC construction. The main features of our approach are: (i) unlike most other approaches that add an extra non-monotone rule layer on top of monotone RDFS, defeasible ρdf\rho df_\bot remains syntactically a triple language and is a simple extension of ρdf\rho df_\bot by introducing some new predicate symbols with specific semantics. In particular, any RDFS reasoner/store may handle them as ordinary terms if it does not want to take account for the extra semantics of the new predicate symbols; (ii) the defeasible ρdf\rho df_\bot entailment decision procedure is build on top of the ρdf\rho df_\bot entailment decision procedure, which in turn is an extension of the one for ρdf\rho df via some additional inference rules favouring an potential implementation; and (iii) defeasible ρdf\rho df_\bot entailment can be decided in polynomial time.Comment: 47 pages. Preprint versio

    Economics of Conflict and Terrorism

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    This book contributes to the literature on conflict and terrorism through a selection of articles that deal with theoretical, methodological and empirical issues related to the topic. The papers study important problems, are original in their approach and innovative in the techniques used. This will be useful for researchers in the fields of game theory, economics and political sciences

    Discovery in Physics

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    Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement

    Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference

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    First CLIPS Conference Proceedings, volume 2

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    The topics of volume 2 of First CLIPS Conference are associated with following applications: quality control; intelligent data bases and networks; Space Station Freedom; Space Shuttle and satellite; user interface; artificial neural systems and fuzzy logic; parallel and distributed processing; enchancements to CLIPS; aerospace; simulation and defense; advisory systems and tutors; and intelligent control

    Experience representation in information systems

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    This thesis looks into the ways subjective dimension of experience could be represented in artificial, non-biological systems, in particular information systems. The pivotal assumption is that experience as opposed to mainstream thinking in information science is not equal to knowledge, so that experience is a broader term which encapsulates both knowledge and subjective, affective component of experience, which so far has not been properly embraced by knowledge representation theories. This is the consequence of dominance of behaviourism and later cognitivism in the XXth-century science, which tended to reduce mind and experience respectively to behavioural expressions and discrete states relating mindful creature to external world, meanwhile the processes of knowing to manipulations with symbols. We support the view that traditional knowledge representation approaches will not suffice to embrace the entirety of mental phenomena. We propose that in order to understand, represent and model the thinking and behavioural processes of mindful entities in information systems we need to look into the phenomenon of experience beyond the boundaries of knowledge. At the same time we propose to look at experience in a more structured way and try to capture it in formal terms, making it amenable to symbolic representation, being aware at the same time of innate limitations of symbolic representations compared to the natural representations in biological bodies. Under the paradigm of mind intentionality, which assumes that minds have this special intrinsic feature that they can relate to external word and thus are about external world, it can be asserted that experience is one in all intentional mind state composed of knowledge that is the intentional contents of this state, the world-to-mind relation, meanwhile its inseparable subjective component is composed of subjective feelings of the mindful individual corresponding to this intentional mind states. If so, we propose that experience can be defined as two-dimensional mental phenomena consisting of mental states that have both knowledge and affective component. Consequently we suggest that experience can be represented as pairs of elements of sets K, and A, where K represents knowledge, hence contents of remembered intentional states of mind (i.e. intentional contents of experience), whereas A represents affect, i.e. the subjective qualitative component of experience. iii Importantly, it does not particularly matter if we define experience as a set of mind states or a mind state process for assessing if the overall relation between knowledge and subjective experience that we have outlined above is valid. Whether there is knowing rather then knowledge or experiencing rather than experience which seems increasingly a contemporary principle, remains a fascinating philosophical, ontological to be more specific, question, however it falls beyond the scope of the thesis and therefore we shall not concentrate on it herewith. Furthermore we propose that the subjective component of experience is also intrinsically intentionalistic, but meanwhile the intentionality in case of knowing is directed outward, to the external world, in case of feeling it is directed inwards to the within of the experiencing mindbody. We tap into the contemporary thinking in the philosophy of mind that the primordial, intrinsic intentionalistic capacity of mind is non-linguistic, as there must be other more primordial, non-linguistic form of intentionality that allows human children, as well as other language-capable animals, to learn language in first place. Contemporary cognitive neuroscience suggest that this capacity is tightly related to affect. We also embrace the theories of consciousness and self coming from brain scientists such as Damasio and Panksepp who believe that there is a primordial component of self, a so called protoself composed of the raw feelings coming from within the body, which are representations of bodily states in the mind, and have strictly subjective character. Therefore we can look at this compound of primordial feelings as a mirror in which external world reflects via the interface of the senses. This results in experience that has this conceptually dual, yet united within the conscious mindbody, composition of intentional contents that is knowledge and subjective component that is built up by feelings coming from within the experiencing mindbody. For it is problematic to state sharply either that this composition is dual or united we can refer to these two separately considered aspects of experience either as components or dimensions. In this thesis we pay particular attention to the role the affective component of experience plays in the behaviour of organisms, and we use the concept of rational agency to discuss the relations between agent experience and behaviour. This role is primarily about motivation and experience vividness, i.e. how easily experiential states can be retrieved from memory. The affective dimension of experience determines the drivers for agent action and influences the remembering and forgetting (memory) processes that experience is prone to. We reflect on how the above presented framework could enhance one of the most popular rational agency models: the Believes Desires Intentions model (BDI) based on Bratmann’s account of practical reason that has dominated information science and artificial intelligence literature. Inspired by Davidson, who opposing Hume’s account that the passions (desires) drive action while reason (belief) merely directs its force, concluded that iv “(...) belief and desire seem equally to be causal conditions of action. But (...) desire is more basic in that if we know enough about a person’s desires, we can work out what he believes, while the reverse does not hold.” (Davidson, 2004) we conclude that in so far as BDI model approaches them, desires are sort of beliefs. Indeed a desire in the above sense is a verbalised desire, i.e. in order for a proposition to be included in the deliberation an agent must have internally verbalize it and accept it by which he converts it into a belief. As a result an agent acquires a belief about its desire. Apart from desires made thus explicit and becoming beliefs there are implicit experiential states that directly influence behaviour, these are not embraced by the Desires set in the BDI and other instrumentalist rationality models as these currently do not have adequate forms of representation. If this is so, the BDI models looses its D creating a gap which must be filled in, which we try to do with the subjective dimension of experience. Under such an account each belief, either the proper one or about the desire, represented formally with a proposition should have an extra component added which would stand for the subjective affective state to this belief. Some preliminary suggestions how this could be implemented are proposed and discussed. The central proposition of this thesis states that experience, broadly understood as the entirety of contents and quality of a conscious mind state, can be satisfactorily represented in information systems, and any information system which objective is to emulate natural agent behaviour with satisfactory faithfulness cannot do without a sound experience representation framework. To achieve this it is necessary to realize and accept, based on convincing evidence from neuroscience, that the missing subjective component of experience is affect that forms and integral part of natural agent’s experience, and determines, or at least impacts profoundly the behaviour of natural agents. Relating affect to knowledge would result in a satisfactory approximation of experience. It is to realize as well that the subjective dimension of experience, classified as affect, is not entirely private, subjective epiphenomenal entity but rather can be studied in objective terms as neurological correlates in the brain following account of emotion and affect as fostered by contemporary neuroscience. By identifying affective correlates of intentional contents of states of mind, which build up knowledge, we can exploit a broader concept experience for the purpose of more accurate emulation of natural agents’ thinking process and behaviour in information systems. This thesis presents and discusses a bulk of evidence coming mainly from three fields: information science, philosophy of mind and cognitive neuroscience that led us to the above stated conclusions, as well as establishes a framework for experience representation in information systems
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