On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds

Data exchange heavily relies on the notion of incomplete database instances. Several semantics for such instances have been proposed and include open (OWA), closed (CWA), and open-closed (OCWA) world. For all these semantics important questions are: whether one incomplete instance semantically implies another; when two are semantically equivalent; and whether a smaller or smallest semantically equivalent instance exists. For OWA and CWA these questions are fully answered. For several variants of OCWA, however, they remain open. In this work we adress these questions for Closed Powerset semantics and the OCWA semantics of [Leonid Libkin and Cristina Sirangelo, 2011]. We define a new OCWA semantics, called OCWA*, in terms of homomorphic covers that subsumes both semantics, and characterize semantic implication and equivalence in terms of such covers. This characterization yields a guess-and-check algorithm to decide equivalence, and shows that the problem is NP-complete. For the minimization problem we show that for several common notions of minimality there is in general no unique minimal equivalent instance for Closed Powerset semantics, and consequently not for the more expressive OCWA* either. However, for Closed Powerset semantics we show that one can find, for any incomplete database, a unique finite set of its subinstances which are subinstances (up to renaming of nulls) of all instances semantically equivalent to the original incomplete one. We study properties of this set, and extend the analysis to OCWA*

On Computing Generalized Backbones

Peer reviewe

Global Inverse Consistency for Interactive Constraint Satisfaction

International audienceSome applications require the interactive resolution of a constraint problem by a human user. In such cases, it is highly desirable that the person who interactively solves the problem is not given the choice to select values that do not lead to solutions. We call this property global inverse consistency. Existing systems simulate this either by maintaining arc consistency after each assignment performed by the user or by compiling offline the problem as a multi-valued decision diagram. In this paper, we define several questions related to global inverse consistency and analyse their complexity. Despite their theoretical intractability, we propose several algorithms for enforcing global inverse consistency and we show that the best version is efficient enough to be used in an interactive setting on several configuration and design problems. We finally extend our contribution to the inverse consistency of tuples

A Preference-Based Approach to Backbone Computation with Application to Argumentation

The backbone of a constraint satisfaction problem consists of those variables that take the same value in all solutions. Algorithms for determining the backbone of propositional formulas, i.e., Boolean satisfiability (SAT) instances, find various real-world applications. From the knowledge representation and reasoning (KRR) perspective, one interesting connection is that of backbones and the so-called ideal semantics in abstract argumentation. In this paper, we propose a new backbone algorithm which makes use of a "SAT with preferences" solver, i.e., a SAT solver which is guaranteed to output a most preferred satisfying assignment w.r.t. a given preference over literals of the SAT instance at hand. We also show empirically that the proposed approach is specifically effective in computing the ideal semantics of argumentation frameworks, noticeably outperforming an other state-of-the-art backbone solver as well as the winning approach of the recent ICCMA 2017 argumentation solver competition in the ideal semantics track.Peer reviewe

Decentralizing MAS Monitoring with DecAMon

We describe DecAMon, an algorithm for decentralizing the monitoring of the MAS communicative behavior described via an Agent Interaction Protocol (AIP). If some agents in the MAS are grouped together and monitored by the same monitor, instead of individually, a partial decentralization of the monitoring activity can still be obtained even if the "unique point of choice" (a.k.a. local choice) and "connectedness for sequence" (a.k.a. causality) coherence conditions are not satisfied by the protocol. Given an AIP specification, DecAMon outputs a set of "Monitoring Safe Partitions" of the agents, namely partitions P which ensure that having one monitor in charge for each group of agents in P allows detection of all and only the protocol violations that a fully centralized monitor would detect. In order to specify AIPs we use "trace expressions": this formalism can express event traces that are not context-free and can model both synchronous and asynchronous communication just by changing the underlying notion of event

Weak, Strong and Dynamic Controllability of Access-Controlled Workflows Under Conditional Uncertainty

A workflow (WF) is a formal description of a business process in which single atomic work units (tasks), organized in a partial order, are assigned to processing entities (agents) in order to achieve some business goal(s). A workflow management system must coordinate the execution of tasks and WF instances. Usually, the assignment of tasks to agents is accomplished by external constraints not represented in a WF. An access-controlled workflow (ACWF) extends a classical WF by explicitly representing agent availability for each task and authorization constraint. Authorization constraints model which users are authorized for which tasks depending on \u201cwho did what\u201d. Recent research has addressed temporal controllability of WFs under conditional and temporal uncertainty. However, controllability analysis for ACWFs under conditional uncertainty has never been addressed before. In this paper, we define weak, strong and dynamic controllability of ACWFs under conditional uncertainty, we present algorithmic approaches to address each of these types of controllability, and we synthesize execution strategies that specify which user has been (or will be) assigned to which task

On Path Consistency for Binary Constraint Satisfaction Problems

Constraint satisfaction problems (CSPs) provide a flexible and powerful framework for modeling and solving many decision problems of practical importance. Consistency properties and the algorithms for enforcing them on a problem instance are at the heart of Constraint Processing and best distinguish this area from other areas concerned with the same combinatorial problems. In this thesis, we study path consistency (PC) and investigate several algorithms for enforcing it on binary finite CSPs. We also study algorithms for enforcing consistency properties that are related to PC but are stronger or weaker than PC. We identify and correct errors in the literature and settle an open question. We propose two improvements that we apply to the well-known algorithms PC-8 and PC-2001, yielding PC-8+ and PC-2001+. Further, we propose a new algorithm for enforcing partial path consistency, σ-∆-PPC, which generalizes features of the well-known algorithms DPC and PPC. We evaluate over fifteen different algorithms on both benchmark and randomly generated binary problems to empirically demonstrate the effectiveness of our approach. Adviser: Berthe Y. Choueir

Effectively Enforcing Minimality During Backtrack Search

Constraint Processing is an expressive and powerful framework for modeling and solving combinatorial decision problems. Enforcing consistency during backtrack search is an effective technique for reducing thrashing in a large search tree. The higher the level of the consistency enforced, the stronger the pruning of inconsistent subtrees. Recently, high-level consistencies (HLC) were shown to be instrumental for solving difficult instances. In particular, minimality, which is guaranteed to prune all inconsistent branches, is advantageous even when enforced locally. In this thesis, we study two algorithms for computing minimality and propose three new mechanisms that significantly improve performance. Then, we integrate the resulting algorithms in a portfolio that operates both locally and dynamically during search. Finally, we empirically evaluate the performance of our approach on benchmark problems. Adviser: Berthe Y. Choueir