The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantic...
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The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantics for hybrid knowledge bases composed of both an ontology (expressed in a fragment of first-order logic) and nonmonotonic rules. These semantics have matured over the years, but only provide solutions for the static case when knowledge does not need to evolve. In this paper we take a first step towards addressing the dynamics of hybrid knowledge bases. We focus on knowledge updates and, considering the state of the art of belief update, ontology update and rule update, we show that current solutions are only partial and difficult to combine. Then we extend the existing work on ABox updates with rules, provide a semantics for such evolving hybrid knowledge bases and study its basic properties. To the best of our knowledge, this is the first time that an update operator is proposed for hybrid knowledge bases.
Inferring a LTLf formula from a set of example traces, also known as passive learning, is a challenging task for model-based techniques. Despite the combinatorial nature of the problem, current state-of-the-art soluti...
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ISBN:
(纸本)9783031492983;9783031492990
Inferring a LTLf formula from a set of example traces, also known as passive learning, is a challenging task for model-based techniques. Despite the combinatorial nature of the problem, current state-of-the-art solutions are based on exhaustive search. They use an example at the time to discard a single candidate formula at the time, instead of exploiting the full set of examples to prune the search space. This hinders their applicability when examples involve many atomic propositions or when the target formula is not small. This short paper proposes the first ILP-based approach for learning LTLf formula from a set of example traces, using a learning from answersets system called ILASP. It compares it to both pure SAT-based techniques and the exhaustive search method. Preliminary experimental results show that our approach improves on previous SAT-based techniques and that has the potential to overcome the limitation of an exhaustive search by optimizing over the full set of examples. Further research directions for the ILP-based LTLf passive learning problem are also discussed.
TropICAL is a Domain Specific Language (DSL) for the description of abstract legal policies. Taking inspiration from narrative tropes, our DSL enables the creation of component "policies" that may be reused ...
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ISBN:
(纸本)9781614997269;9781614997252
TropICAL is a Domain Specific Language (DSL) for the description of abstract legal policies. Taking inspiration from narrative tropes, our DSL enables the creation of component "policies" that may be reused between case descriptions. These components are compiled to social institutions, which are realised in answer set programming (ASP) code. In this way, the actions of defendant and plaintiff take the shape of a story which must conform to the rules in the ASP description. We propose the use of our DSL in a tool designed for lawyers to generate arguments for the argumentation process.
Reasoning about Action and Change (RAC) and answer set programming (ASP) are two well-known fields in AI for logic-based reasoning. Each paradigm bears unique features and a possible integration can lead to more effec...
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ISBN:
(纸本)9783319070636;9783319070643
Reasoning about Action and Change (RAC) and answer set programming (ASP) are two well-known fields in AI for logic-based reasoning. Each paradigm bears unique features and a possible integration can lead to more effective ways to address hard AI problems. In this paper, we report on implementations that embed RAC formalisms and concepts in ASP and present the experimental results obtained, building on a graph-based problem setting that introduces casual and temporal requirements.
Epistemic Logic Programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets. Different semantic appr...
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ISBN:
(纸本)9783031157073;9783031157066
Epistemic Logic Programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property, that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to 'traditional' ASP. We analyze the possibility to change the perspective, shifting from a bottom-up to a top-down approach to splitting. Our new definition: (i) copes with concerns regarding unfoundedness of world views and subjective constraint monotonicity;(ii) is provably applicable to many of the existing semantics;(iii) operates similarly to "traditional" ASP;(iv) provably coincides with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified).
answer set programming (ASP) is a powerful form of declarative programming used in areas such as planning or reasoning. ASP solvers enforce stable model semantics, which rule out solutions representing certain kinds o...
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answer set programming (ASP) is a powerful form of declarative programming used in areas such as planning or reasoning. ASP solvers enforce stable model semantics, which rule out solutions representing certain kinds of circular reasoning. Unfortunately, current ASP solvers are incapable of solving problems involving cyclic dependencies between multiple integer or continuous quantities effectively. In this paper, we generalize the notion of stable models to bound founded variables with arbitrary domains, where bounds on such variables need to be justified by some rule in the program in order for the model to be stable. We show how to handle significantly more general rule forms where bound founded variables can act as head or body variables, and where head and body variables can be related via complex constraints subject to certain monotonicity requirements. We describe a new unfounded set detection algorithm which allows us to enforce this generalization of the stable model semantics. We also show how these unfounded sets can be explained in order to allow effective conflict-directed clause learning. The new solver merges the best features of CP, SAT and ASP solvers and allows new types of problems to be solved very efficiently.
The existing methods for automatically implementing access control policies are mainly traditional logic programming, that is, monotonic logic. There are some shortcomings in monotonic logic, the most important of whi...
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ISBN:
(纸本)9781450361910
The existing methods for automatically implementing access control policies are mainly traditional logic programming, that is, monotonic logic. There are some shortcomings in monotonic logic, the most important of which is that its design does not invalidate the initial belief in later observation. This limitation makes traditional logic methods not suitable for modeling and analyzing context-aware access control policies. When we need to enforce exceptions at runtime, inconsistencies and conflicts occur that cannot invalidate the initial strategy. Therefore, we propose a non-monotonic logic-based adaptive access control policy modeling and analysis inference scheme that can expressly indicate unavailable context data and incomplete access control policies. In this article, a formal method, Question setprogramming (ASP), is proposed to elegantly represent the unavailability of context data. In the following, we will automatically learn conflict resolution methods through cautious induction learning methods to make the modeling and analysis of adaptive access control policies more comprehensive and intelligent.
Update of knowledge bases is becoming an important topic in Artificial Intelligence and a key problem in knowledge representation and reasoning. One of the latest ideas to update logic programs is choosing between mod...
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ISBN:
(纸本)9783540766308
Update of knowledge bases is becoming an important topic in Artificial Intelligence and a key problem in knowledge representation and reasoning. One of the latest ideas to update logic programs is choosing between models of Minimal Generalised answersets to overcome disadvantages of previous approaches. This paper describes an implementation of the declarative version of updates sequences that has been proposed as an alternative to syntax-based semantics. One of the main contributions of this implementation is to use DLV's Weak Constraints to compute the model(s) of an update sequence, besides presenting the precise definitions proposed by the authors and an online solver. As a result;the paper makes an outline of the basic structure of the system, describes the employed technology, discusses the major process of computing the models;and illustrates the system through examples.
In the heterogeneous and dynamic Internet of Things (IoT), applications and services are frequently subject to change for various reasons such as maintaining their functionality, reliability, availability, and perform...
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ISBN:
(纸本)9781450365390
In the heterogeneous and dynamic Internet of Things (IoT), applications and services are frequently subject to change for various reasons such as maintaining their functionality, reliability, availability, and performance. Detecting and communicating these changes are still performed manually by responsible developers and administrators. Such a mechanism will not be adequate anymore in the future of large-scale IoT environments. Therefore, we present a comprehensive framework named DECOM for automatic detection and communication of service changes. Here, we assume that capabilities and interfaces of IoT devices are described and provided through REST services. To be able to detect syntactic as well as semantic changes, we transform an extended version of the interface description into a logic program and apply a sequence of analysis steps to detect changes. The feasibility and applicability of the framework are demonstrated in an IoT application scenario.
answerset programs with time predicates are useful to model systems whose properties depend on time, like for example gene regulatory networks. A state of such a system at time point t then corresponds to the literal...
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ISBN:
(纸本)9783939897170
answerset programs with time predicates are useful to model systems whose properties depend on time, like for example gene regulatory networks. A state of such a system at time point t then corresponds to the literals of an answerset that are grounded with time constant t. An important task when modelling time-dependent systems is to find steady states from which the system's behaviour does not change anymore. This task is complicated by the fact that it is typically not known in advance at what time steps these steady states occur. A brute force approach of estimating a time upper bound t(max) and grounding and solving the program w.r.t. that upper bound leads to a suboptimal solving time when the estimate is too low or too high. In this paper we propose a more efficient algorithm for solving Markovian programs, which are time-dependent programs for which the next state depends only on the previous state. Instead of solving these Markovian programs for a long time interval {0,...,t(max)}, we successively find answersets of parts of the grounded program. Our approach guarantees the discovery of all steady states and cycles while avoiding unnecessary extra work.
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