Several extensions of the stable model semantics are available to describe "intensional" functions-functions that can be described in terms of other functions and predicates by logic programs. Such functions...
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Several extensions of the stable model semantics are available to describe "intensional" functions-functions that can be described in terms of other functions and predicates by logic programs. Such functions are useful for expressing inertia and default behaviors of systems, and can be exploited for alleviating the grounding bottleneck involving functional fluents. However, the extensions were defined in different ways under different intuitions. In this paper we provide several reformulations of the extensions, and note that they are in fact closely related to each other and coincide on large syntactic classes of logic programs.
the proceedings contain 34 papers. the topics discussed include: the logic of contextuality;the best a monitor can do;are two binary operators necessary to finitely axiomatise parallel composition?;a quasi-polynomial ...
ISBN:
(纸本)9783959771757
the proceedings contain 34 papers. the topics discussed include: the logic of contextuality;the best a monitor can do;are two binary operators necessary to finitely axiomatise parallel composition?;a quasi-polynomial black-box algorithm for fixed point evaluation;learning concepts described by weight aggregation logic;open bar – a Brouwerian intuitionistic logic with a pinch of excluded middle;discounted-sum automata with multiple discount factors;reachability in distributed memory automata;learning automata and transducers: a categorical approach;semiring provenance for fixed-point logic;extension preservation in the finite and prefix classes of first order logic;and decidable entailments in separation logic withinductive definitions: beyond establishment.
the proceedings contain 29 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Knowledge discovery in databases;on the complexity of some inductivelogicprogramming pro...
ISBN:
(纸本)3540635149
the proceedings contain 29 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Knowledge discovery in databases;on the complexity of some inductivelogicprogramming problems;inductivelogicprogramming and constraint logicprogramming;learning phonetic rules in a speech recognition system;cautious induction in inductivelogicprogramming;generating numerical literals during refinement;lookahead and discretization in ILP;the application of progol to a database of enantioseparations;part-of-speech tagging using progol;maximum entropy modeling with clausal constraints;mining association rules in multiple relations;using logical decision trees for clustering;induction of slovene nominal paradigms;normal forms for inductivelogicprogramming;induction of logic programs with more than one recursive clause by analyzing saturations;a logical framework for graph theoretical decision tree learning;learning with abduction;systematic predicate invention in inductivelogicprogramming;learning programs in the event calculus;distance between herbrand interpretations;realizing progol by forward reasoning;probabilistic first-order classification;learning horn definitions with equivalence and membership queries;using abstraction schemata in inductivelogicprogramming and distance induction in first order logic.
We consider the satisfiability problem for a fragment of separation logic including inductive predicates with shape and arithmetic properties. We show that the fragment is decidable if the arithmetic properties can be...
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ISBN:
(纸本)9783319633909;9783319633893
We consider the satisfiability problem for a fragment of separation logic including inductive predicates with shape and arithmetic properties. We show that the fragment is decidable if the arithmetic properties can be represented as semilinear sets. Our decision procedure is based on a novel algorithm to infer a finite representation for each inductive predicate which precisely characterises its satisfiability. Our analysis shows that the proposed algorithm runs in exponential time in the worst case. We have implemented our decision procedure and integrated it into an existing verification system. Our experiment on benchmarks shows that our procedure helps to verify the benchmarks effectively.
the paper provides a framework for the verification of business processes, based on an extension of answer set programming (ASP) with temporal logic and constraints. the framework allows to capture expressive fluent a...
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the paper provides a framework for the verification of business processes, based on an extension of answer set programming (ASP) with temporal logic and constraints. the framework allows to capture expressive fluent annotations as well as data awareness in a uniform way. It allows for a declarative specification of a business process but also for encoding processes specified in conventional workflow languages. Verification of temporal properties of a business process, including verification of compliance to business rules, is performed by bounded model checking techniques in Answer Set programming, extended with constraint solving for dealing with conditions on numeric data.
the success of Computer Vision (CV) relies heavily on manually annotated data. However, it is prohibitively expensive to annotate images in key domains such as healthcare, where data labeling requires significant doma...
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ISBN:
(纸本)9798400701030
the success of Computer Vision (CV) relies heavily on manually annotated data. However, it is prohibitively expensive to annotate images in key domains such as healthcare, where data labeling requires significant domain expertise and cannot be easily delegated to crowd workers. To address this challenge, we propose a neuro-symbolic approach called Rapid, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules. Specifically, Rapid combines pre-trained CV models and inductivelogic learning to infer the logic-based labeling rules. Rapid achieves a labeling accuracy of 83.33% to 88.33% on four image labeling tasks with only 12 to 39 labeled samples. In particular, Rapid significantly outperforms finetuned CV models in two highly specialized tasks. these results demonstrate the effectiveness of Rapid in learning from small data and its capability to generalize among different tasks. Code and our dataset are publicly available at https://***/Neural-Symbolic-Image-Labeling/Rapid/
Maher (2012) introduced an approach for relative expressiveness of defeasible logics, and two notions of relative expressiveness were investigated. Using the first of these definitions of relative expressiveness, we s...
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Maher (2012) introduced an approach for relative expressiveness of defeasible logics, and two notions of relative expressiveness were investigated. Using the first of these definitions of relative expressiveness, we show that all the defeasible logics in the DL framework are equally expressive under this formulation of relative expressiveness. the second formulation of relative expressiveness is stronger than the first. However, we show that logics incorporating individual defeat are equally expressive as the corresponding logics with team defeat. thus the only differences in expressiveness of logics in DL arise from differences in how ambiguity is handled. this completes the study of relative expressiveness in DL begun in Maher (2012).
We consider disjunctive logic programs without function symbols but with existential quantification in rule heads, under the semantics of general stable models. there are at least two interesting prospects in these pr...
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We consider disjunctive logic programs without function symbols but with existential quantification in rule heads, under the semantics of general stable models. there are at least two interesting prospects in these programs. the first is that a program can be made more succinct by using existential variables, and the second is on the potential in representing defeasible ontological knowledge by these logic programs. this paper studies some of the properties of these programs. First, we show a simple yet intuitive definition of stable models for these programs that does not resort to second-order logic. Second, the stable models of these programs can be characterized by an extension of progression for disjunctive programs, which provides a native characterization of justification for stable models. We then study the decidability issue. While the stable model existence problem for safe disjunctive programs is decidable, with existential quantification allowed in rule heads the problem becomes undecidable. We identify an interesting decidable fragment by exploring a new notion of stratification over existential quantification.
In information security, representation and reasoning of authorization policy has been a key research topic in this field especially in a sophisticated information sharing and exchange environment [6, 7, 9]. In such a...
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ISBN:
(纸本)9781943436040
In information security, representation and reasoning of authorization policy has been a key research topic in this field especially in a sophisticated information sharing and exchange environment [6, 7, 9]. In such a scenario, an user's request to access the system may not be able to decide straightaway, it may initiate a sequence of complex executions of authorization commands in order to determine either to grant or deny such a request. Becker and Nanz's logic of State-Modifying Policies (SMP) is a formal system addressing such problem in access control. In this paper, we provide a declarative semantics for SMP through a translation from SMP to Answer Set programming (ASP) and propose a system prototype to implement our approach. Our experimental results show that our ASP implementation for SMP reasoning is effective to deal with real world problem domains. Copyright ISCA, CAINE 2016.
Dealing with domains involving substantial quantitative information in Answer Set programming (ASP) often results in cumbersome and inefficient encodings. Hybrid "CASP" languages combining ASP and Constraint...
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Dealing with domains involving substantial quantitative information in Answer Set programming (ASP) often results in cumbersome and inefficient encodings. Hybrid "CASP" languages combining ASP and Constraint programming aim to overcome this limitation, but also impose inconvenient constraints - first and foremost that quantitative information must be encoded by means of total functions. this goes against central knowledge representation principles that contribute to the power of ASP, and makes the formalization of certain domains difficult. ASP{f} is being developed withthe ultimate goal of providing scientists and practitioners with an alternative to CASP languages that allows for the efficient representation of qualitative and quantitative information in ASP without restricting one's ability to deal with incompleteness or uncertainty. In this paper we present the latest outcome of such research: versions of the language and of the supporting system that allow for practical, industrial-size use and scalability. the applicability of ASP{f} is demonstrated by a case study on an actual industrial application.
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