603 research outputs found
On the Parameterized Complexity and Kernelization of the Workflow Satisfiability Problem
A workflow specification defines a set of steps and the order in which those
steps must be executed. Security requirements may impose constraints on which
groups of users are permitted to perform subsets of those steps. A workflow
specification is said to be satisfiable if there exists an assignment of users
to workflow steps that satisfies all the constraints. An algorithm for
determining whether such an assignment exists is important, both as a static
analysis tool for workflow specifications, and for the construction of run-time
reference monitors for workflow management systems. Finding such an assignment
is a hard problem in general, but work by Wang and Li in 2010 using the theory
of parameterized complexity suggests that efficient algorithms exist under
reasonable assumptions about workflow specifications. In this paper, we improve
the complexity bounds for the workflow satisfiability problem. We also
generalize and extend the types of constraints that may be defined in a
workflow specification and prove that the satisfiability problem remains
fixed-parameter tractable for such constraints. Finally, we consider
preprocessing for the problem and prove that in an important special case, in
polynomial time, we can reduce the given input into an equivalent one, where
the number of users is at most the number of steps. We also show that no such
reduction exists for two natural extensions of this case, which bounds the
number of users by a polynomial in the number of steps, provided a
widely-accepted complexity-theoretical assumption holds
Abduction in Well-Founded Semantics and Generalized Stable Models
Abductive logic programming offers a formalism to declaratively express and
solve problems in areas such as diagnosis, planning, belief revision and
hypothetical reasoning. Tabled logic programming offers a computational
mechanism that provides a level of declarativity superior to that of Prolog,
and which has supported successful applications in fields such as parsing,
program analysis, and model checking. In this paper we show how to use tabled
logic programming to evaluate queries to abductive frameworks with integrity
constraints when these frameworks contain both default and explicit negation.
The result is the ability to compute abduction over well-founded semantics with
explicit negation and answer sets. Our approach consists of a transformation
and an evaluation method. The transformation adjoins to each objective literal
in a program, an objective literal along with rules that ensure
that will be true if and only if is false. We call the resulting
program a {\em dual} program. The evaluation method, \wfsmeth, then operates on
the dual program. \wfsmeth{} is sound and complete for evaluating queries to
abductive frameworks whose entailment method is based on either the
well-founded semantics with explicit negation, or on answer sets. Further,
\wfsmeth{} is asymptotically as efficient as any known method for either class
of problems. In addition, when abduction is not desired, \wfsmeth{} operating
on a dual program provides a novel tabling method for evaluating queries to
ground extended programs whose complexity and termination properties are
similar to those of the best tabling methods for the well-founded semantics. A
publicly available meta-interpreter has been developed for \wfsmeth{} using the
XSB system.Comment: 48 pages; To appear in Theory and Practice in Logic Programmin
Program Verification with Separation Logic
International audienceSeparation Logic is a framework for the development of modular program analyses for sequential, inter-procedural and concurrent programs. The first part of the paper introduces Separation Logic first from a historical, then from a program verification perspective. Because program verification eventually boils down to deciding logical queries such as the validity of verification conditions, the second part is dedicated to a survey of decision procedures for Separation Logic, that stem from either SMT, proof theory or automata theory. Incidentally we address issues related to decidability and computational complexity of such problems, in order to expose certain sources of intractability
Hunting for Tractable Languages for Judgment Aggregation
Judgment aggregation is a general framework for collective decision making
that can be used to model many different settings. Due to its general nature,
the worst case complexity of essentially all relevant problems in this
framework is very high. However, these intractability results are mainly due to
the fact that the language to represent the aggregation domain is overly
expressive. We initiate an investigation of representation languages for
judgment aggregation that strike a balance between (1) being limited enough to
yield computational tractability results and (2) being expressive enough to
model relevant applications. In particular, we consider the languages of Krom
formulas, (definite) Horn formulas, and Boolean circuits in decomposable
negation normal form (DNNF). We illustrate the use of the positive complexity
results that we obtain for these languages with a concrete application: voting
on how to spend a budget (i.e., participatory budgeting).Comment: To appear in the Proceedings of the 16th International Conference on
Principles of Knowledge Representation and Reasoning (KR 2018
Parameterized aspects of team-based formalisms and logical inference
Parameterized complexity is an interesting subfield of complexity theory that has received a lot of attention in recent years. Such an analysis characterizes the complexity of (classically) intractable problems by pinpointing the computational hardness to some structural aspects of the input. In this thesis, we study the parameterized complexity of various problems from the area of team-based formalisms as well as logical inference.
In the context of team-based formalism, we consider propositional dependence logic (PDL). The problems of interest are model checking (MC) and satisfiability (SAT). Peter Lohmann studied the classical complexity of these problems as a part of his Ph.D. thesis proving that both MC and SAT are NP-complete for PDL. This thesis addresses the parameterized complexity of these problems with respect to a wealth of different parameterizations.
Interestingly, SAT for PDL boils down to the satisfiability of propositional logic as implied by the downwards closure of PDL-formulas. We propose an interesting satisfiability variant (mSAT) asking for a satisfiable team of size m. The problem mSAT restores the ‘team semantic’ nature of satisfiability for PDL-formulas. We propose another problem (MaxSubTeam) asking for a maximal satisfiable team if a given team does not satisfy the input formula.
From the area of logical inference, we consider (logic-based) abduction and argumentation. The problem of interest in abduction (ABD) is to determine whether there is an explanation for a manifestation in a knowledge base (KB). Following Pfandler et al., we also consider two of its variants by imposing additional restrictions over the size of an explanation (ABD and ABD=). In argumentation, our focus is on the argument existence (ARG), relevance (ARG-Rel) and verification (ARG-Check) problems. The complexity of these problems have been explored already in the classical setting, and each of them is known to be complete for the second level of the polynomial hierarchy (except for ARG-Check which is DP-complete) for propositional logic. Moreover, the work by Nord and Zanuttini (resp., Creignou et al.) explores the complexity of these problems with respect to various restrictions over allowed KBs for ABD (ARG). In this thesis, we explore a two-dimensional complexity analysis for these problems. The first dimension is the restrictions over KB in Schaefer’s framework (the same direction as Nord and Zanuttini and Creignou et al.). What differentiates the work in this thesis from an existing research on these problems is that we add another dimension, the parameterization.
The results obtained in this thesis are interesting for two reasons. First (from a theoretical point of view), ideas used in our reductions can help in developing further reductions and prove (in)tractability results for related problems. Second (from a practical point of view), the obtained tractability results might help an agent designing an instance of a problem come up with the one for which the problem is tractable
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Geometric Representation Learning
Vector embedding models are a cornerstone of modern machine learning methods for knowledge representation and reasoning. These methods aim to turn semantic questions into geometric questions by learning representations of concepts and other domain objects in a lower-dimensional vector space. In that spirit, this work advocates for density- and region-based representation learning. Embedding domain elements as geometric objects beyond a single point enables us to naturally represent breadth and polysemy, make asymmetric comparisons, answer complex queries, and provides a strong inductive bias when labeled data is scarce. We present a model for word representation using Gaussian densities, enabling asymmetric entailment judgments between concepts, and a probabilistic model for weighted transitive relations and multivariate discrete data based on a lattice of axis-aligned hyperrectangle representations (boxes). We explore the suitability of these embedding methods in different regimes of sparsity, edge weight, correlation, and independence structure, as well as extensions of the representation and different optimization strategies. We make a theoretical investigation of the representational power of the box lattice, and propose extensions to address shortcomings in modeling difficult distributions and graphs
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