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40th Annual IEEE Computer Software and Applications Conference Symposium (COMPSAC) / Symposium on Software Engineering Technology and Applications (setA)

作者： Sartoli, Sara Namin, Akbar Siami Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781467388450

many of the current policy-based management systems implement policies that depend on dynamic operational environment contexts. The existing formal-based approaches for enforcing security policies are mainly expressed using first-order logic. A major drawback of using first-order logic in implementing dynamic policies is that new observations cannot override previously inferred consequences. In fact, a security system whose enterprise policies are implemented using first-order logic is required to have complete access to data in advance in order to be able to perform an informed reasoning and enforce restricting policies. As a major problem, the systems designed based on these first order logic-based approaches are often static, inflexible, and hard to manage and scale. This paper introduces an approach for expressing and enforcing adaptive access control policies dynamically. The paper presents a non-monotonic formal approach based on answer set programming where default policies are explicitly separated from context-dependent and exception policies that often occur in dynamic systems and in particular when the required context data are unavailable ahead of time. The results of presented case study demonstrate the flexibility of the proposed approach compared to the first order logic-based context-sensitive approaches as implemented in Organizational-Based Access Control (ORBAC) model.

关键词： Access Control Formal Analysis Adaptive Security Policies Management answer set programming

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Experiences Running a Parallel answer set Solver on Blue Gene

Experiences Running a Parallel Answer Set Solver on Blue Gen...

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16th European Parallel-Virtual-Machine-and-Message-Passing-Interface-Users-Group Meeting (PVM/MPI)

作者： Schneidenbach, Lars Schnor, Bettina Gebser, Martin Kaminski, Roland Kaufmann, Benjamin Schaub, Torsten IBM Ireland Dublin Software Lab Dublin 15 Ireland Univ Potsdam Inst Informat D-14482 Potsdam Germany

ISBN: (纸本)9783642037696

This paper presents the concept of parallelisation of a solver for answer set programming (ASP). While there already exist some approaches to parallel ASP solving, there was a lack of a parallel version of the powerful clasp solver. We implemented a parallel version of clasp based on message-passing. Experimental results on Blue Gene P/L indicate the potential of such an approach.

关键词： Applications based on Message-Passing answer set programming Performance evaluation

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Towards an ILP Application in Machine Ethics 1

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29th International Conference on Inductive Logic programming

作者： Dyoub, Abeer Costantini, Stefania Lisi, Francesca A. Univ Aquila Dipartimento Ingn & Sci Informaz & Matemat Laquila Italy Univ Bari Aldo Moro Dipartimento Informat Bari Italy Univ Bari Aldo Moro Ctr Interdipartimentale Log & Applicaz Bari Italy

ISBN: (数字)9783030492106

ISBN: (纸本)9783030492090;9783030492106

Machine Ethics is a newly emerging interdisciplinary field which is concerned with adding an ethical dimension to Artificial Intelligent (AI) agents. In this paper we address the problem of representing and acquiring rules of codes of ethics in the online customer service domain. The proposed solution approach relies on the non-monotonic features of answer set programming (ASP) and applies ILP. The approach is illustrated by means of examples taken from the preliminary tests conducted with a couple of state-of-the-art ILP algorithms for learning ASP rules.

关键词： answer set programming Machine Ethics ILP Applications

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Teaching Commonsense and Dynamic Knowledge to Service Robots 11th

Teaching Commonsense and Dynamic Knowledge to Service Robots

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11th International Conference on Social Robotics (ICSR)

作者： Opfer, Stephan Jakob, Stefan Geihs, Kurt Univ Kassel Wilhelmshoher Allee 73 D-34121 Kassel Germany

ISBN: (纸本)9783030358884;9783030358877

Incorporating commonsense and coping with dynamic knowledge are key capabilities of service robots to efficiently interact with humans. In the presented system, we demonstrate how to equip service robots with commonsense knowledge and the dynamic reasoning capabilities of answer set programming (ASP). We investigated the response of our system to basic human needs and evaluated the viability and scalability of the combination of the commonsense knowledge database ConceptNet 5 and the ASP solver Clingo. Our results show the flexibility and versatility of our approach. Further, we identified the need for research on scalability in case of environments that are abundant with objects.

关键词： Commonsense reasoning Dynamic knowledge answer set programming Service robots Cognitive Robotics Human-robot interaction

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The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning 18th

The Role of Foundation Models in Neuro-Symbolic Learning and...

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18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy)

作者： Cunnington, Daniel Law, Mark Lobo, Jorge Russo, Alessandra Durable Ai Louisville CO 80027 USA Imperial Coll London London England ILASP Ltd Grantham England Univ Pompeu Fabra Barcelona Spain IBM Res Yorktown Hts NY USA

ISBN: (纸本)9783031711664;9783031711671

Neuro-Symbolic AI (NeSy) holds promise to ensure the safe deployment of AI systems, as interpretable symbolic techniques provide formal behaviour guarantees. The challenge is how to effectively integrate neural and symbolic computation, to enable learning and reasoning from raw data. Existing pipelines that train the neural and symbolic components sequentially require extensive labelling, whereas end-to-end approaches are limited in terms of scalability, due to the combinatorial explosion in the symbol grounding problem. In this paper, we leverage the implicit knowledge within foundation models to enhance the performance in NeSy tasks, whilst reducing the amount of data labelling and manual engineering. We introduce a new architecture, called NeSyGPT, which fine-tunes a vision-language foundation model to extract symbolic features from raw data, before learning a highly expressive answer set program to solve a downstream task. Our comprehensive evaluation demonstrates that NeSyGPT has superior accuracy over various baselines, and can scale to complex NeSy tasks. Finally, we highlight the effective use of a large language model to generate the programmatic interface between the neural and symbolic components, significantly reducing the amount of manual engineering required. The Appendix is presented in the longer version of this paper, which contains additional results and analysis [8].

关键词： Neuro-Symbolic Learning Foundation Models answer set programming

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DeduDeep: An Extensible Framework for Combining Deep Learning and ASP-Based Models 16th

DeduDeep: An Extensible Framework for Combining Deep Learnin...

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16th International Conference on Logic programming and Non-Monotonic Reasoning (LPNMR)

作者： Bruno, Pierangela Calimeri, Francesco Marte, Cinzia Univ Calabria Dept Math & Comp Sci Arcavacata Di Rende Italy

ISBN: (纸本)9783031157073;9783031157066

In the last decades, Deep Learning (DL)-based approaches have been fruitfully employed in many tasks, such as providing valuable support to computer-aided diagnosis and medicine. However, DL-based approaches are known to suffer from some limitations;for instance, they lack of proper means for providing clear explanations and interpretations of the results, or explicitly including available knowledge to drive decisions. In this work, we present DeduDeep, the prototypical implementation of a framework explicitly conceived with the aim of tackling such limitations by making use of deductive declarative formalisms. In particular, the framework aims at enabling the declarative encoding of explicit knowledge, and, by relying on the use of answer set programming (ASP), taking advantage of it for driving decisions taken by neural networks and refining the output. The framework has been tested using different artificial neural networks tailored to semantic segmentation tasks over Laryngeal Endoscopic Images and Freiburg Sitting People Images.

关键词： answer set programming Knowledge Representation and Reasoning Non-monotonic reasoning Deep Learning Semantic Segmentation Inductive-deductive coupling

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Weighted knowledge bases with typicality and defeasible reasoning in a gradual argumentation semantics

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INTELLIGENZA ARTIFICIALE 2024年第1期18卷 153-174页

作者： Alviano, Mario Giordano, Laura Dupre, Daniele Theseider Univ Calabria Dipartimento Matemat & Informat DEMACS Arcavacata Di Rende Italy Univ Piemonte Orientale Dipartimento Sci & Innovaz Tecnolog DISIT Viale Michel 11 I-15121 Alessandria Italy

. Weighted knowledge bases for description logics with typicality provide a logical interpretation of MultiLayer Perceptrons, based on a "concept-wise" multi-preferential semantics. On the one hand, in the finitely many-valued case, answer set programming (ASP) has been shown to be suitable for addressing defeasible reasoning from weighted knowledge bases for the boolean fragment of ALC. . On the other hand, the semantics of weighted knowledge bases with typicality, in their different variants, have suggested some new gradual argumentation semantics, as well as an approach for defeasible reasoning over a weighted argumentation graph, building on the gradual semantics and, specifically on the (p-coherent semantics. In this paper, we explore the relationships between weighted knowledge bases and weighted argumentation graphs, to develop proof methods for defeasible reasoning over an argumentation graph under the (p-coherent semantics, in the finitely-valued case. We establish a mapping from a weighted argumentation graph to a weighted knowledge base as well as a lower bound on the complexity of the problem of verifying graded implications over an argumentation graph in the (p-coherent semantics. We also consider a mapping from weighted knowledge bases to weighted argumentation graphs, and provide an ASP implementation and some experimental results.

关键词： Preferential semantics description logic many-valued logic answer set programming gradual argumentation

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Reasoning for Autonomous Agents in Dynamic Domains: Towards Automatic Satisfaction of the Module Property 1

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9th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Opfer, Stephan Jakob, Stefan Geihs, Kurt Univ Kassel Distributed Syst Res Grp Wilhelmshoher Allee 73 Kassel Germany

ISBN: (数字)9783319935812

ISBN: (纸本)9783319935812;9783319935805

State-of-the-art service robots that fetch a cup of coffee and clean up rooms require cognitive skills such as learning, planning, and reasoning. Especially reasoning in dynamic and human populated environments demands for novel approaches that can handle comprehensive and fluent knowledge bases. Our long-term objective is an autonomous robotic team that is capable of handling dynamic and domestic environments. Therefore, we combined ALICA - A Language for Interactive Cooperative Agents - with the answer set programming solver Clingo. The answer set programming approach offers multi-shot solving techniques and non-monotonic stable model semantics, but requires to keep the Module Property satisfied. We developed an automatic satisfaction of the Module Property and chose topological path planning as our evaluation scenario. We utilised the Region Connection Calculus as the underlying formalism of our evaluation and investigated the scalability of our implementation. The results show that our approach handles dynamic environments and scales up to appropriately large problem sizes while automatically satisfying the Module Property.

关键词： answer set programming Region Connection Calculus Module Property Multi-shot solving

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Semantic Inference for Pharmacokinetic Drug-Drug Interactions

Semantic Inference for Pharmacokinetic Drug-Drug Interaction...

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8th IEEE International Conference on Semantic Computing

作者： Moitra, Abha Palla, Ravi Tari, Luis Krishnamoorthy, Mukkai GE Global Res Knowledge Discovery Lab Niskayuna NY 12309 USA Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

ISBN: (纸本)9781479940035

Drug-drug interaction (DDI) study is an important aspect of therapy management and drug efficacy. DDI study investigates how drugs interact with each other and determine whether these interactions may lead to dire effects or nullify the therapeutic effects of each other. In this paper we model metabolic pathways of drugs that include the reaction effects between drugs and the related enzymes. By modeling the reaction effects, our model captures the degree of the effects of the interacting drugs. We introduce a novel methodology that combines semantics, ontology to model the concepts and interactions, and answer set programming for temporal reasoning. We illustrate our method by inferring the effects of DDI among three drugs clozapine, olanzapine and fluvoxamine.

关键词： Drug-Drug Interactions Ontology Semantics Temporal Reasoning answer set programming

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Results in Workflow Resiliency Complexity, New Formulation, and ASP Encoding 19

Results in Workflow Resiliency Complexity, New Formulation, ...

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9th ACM Conference on Data and Application Security and Privacy (CODASPY)

作者： Fong, Philip W. L. Univ Calgary Calgary AB Canada

ISBN: (纸本)9781450360999

First proposed by Wang and Li in 2007, workflow resiliency is a policy analysis for ensuring that, even when an adversarial environment removes a subset of workers from service, a workflow can still be instantiated to satisfy all the security constraints. Wang and Li proposed three notions of workflow resiliency: static, decremental, and dynamic resiliency. While decremental and dynamic resiliency are both PSPACE-complete, Wang and Li did not provide a matching lower and upper bound for the complexity of static resiliency. The present work begins with proving that static resiliency is Pi(p)(2)-complete, thereby bridging a long-standing complexity gap in the literature. In addition, a fourth notion of workflow resiliency, one-shot resiliency, is proposed and shown to remain in the third level of the polynomial hierarchy. This shows that sophisticated notions of workflow resiliency need not be PSPACE-complete. Lastly, we demonstrate how to reduce static and one-shot resiliency to answer set programming (ASP), a modern constraint-solving technology that can be used for solving reasoning tasks in the lower levels of the polynomial hierarchy. In summary, this work demonstrates the value of focusing on notions of workflow resiliency that reside in the lower levels of the polynomial hierarchy.

关键词： Availability Workflow authorization model Workflow resiliency Static resiliency One-shot resiliency answer set programming

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