23 research outputs found
A Rational and Efficient Algorithm for View Revision in Databases
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In this paper, we argue that to apply rationality result of belief dynamics
theory to various practical problems, it should be generalized in two respects:
first of all, it should allow a certain part of belief to be declared as
immutable; and second, the belief state need not be deductively closed. Such a
generalization of belief dynamics, referred to as base dynamics, is presented,
along with the concept of a generalized revision algorithm for Horn knowledge
bases. We show that Horn knowledge base dynamics has interesting connection
with kernel change and abduction. Finally, we also show that both variants are
rational in the sense that they satisfy certain rationality postulates stemming
from philosophical works on belief dynamics
A New Rational Algorithm for View Updating in Relational Databases
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In order to apply the rationality result of belief dynamics theory to various
practical problems, it should be generalized in two respects: first it should
allow a certain part of belief to be declared as immutable; and second, the
belief state need not be deductively closed. Such a generalization of belief
dynamics, referred to as base dynamics, is presented in this paper, along with
the concept of a generalized revision algorithm for knowledge bases (Horn or
Horn logic with stratified negation). We show that knowledge base dynamics has
an interesting connection with kernel change via hitting set and abduction. In
this paper, we show how techniques from disjunctive logic programming can be
used for efficient (deductive) database updates. The key idea is to transform
the given database together with the update request into a disjunctive
(datalog) logic program and apply disjunctive techniques (such as minimal model
reasoning) to solve the original update problem. The approach extends and
integrates standard techniques for efficient query answering and integrity
checking. The generation of a hitting set is carried out through a hyper
tableaux calculus and magic set that is focused on the goal of minimality.Comment: arXiv admin note: substantial text overlap with arXiv:1301.515
Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version)
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.Comment: 40 pages; an extended version of the article published in Theory and
Practice of Logic Programming, 10 (4-6): 547 - 564, July. Copyright 2010
Cambridge University Pres
Approximate Compliance Checking for Annotated Process Models
We describe a method for validating whether the states reached by a process are compliant with a set of constraints. This serves to (i) check the compliance of a new or altered process against the constraints base, and (ii) check the whole process repository against a changed constraints base, e.g., when new regulations come into being. For these purposes we formalize a particular class of compliance rules as well as annotated process models, the latter by combining a notion from the workflow literature with a notion from the AI actions and change literature. The compliance rules in turn pose restrictions on the desirable states. Each rule takes the form of a clausal constraint, i.e., a disjunction of literals. If for a given state there is a grounded clause none of whose literals are true, then the constraint is violated and indicates non-compliance. Checking whether a process is compliant with the rules involves enumerating all reachable states and is in general a hard search problem. Since long waiting times during process modelling are undesirable, it is important to explore restricted classes and approximate methods. We present a polynomial-time algorithm that, for a particular class of processes, computes the sets of literals that are necessarily true at particular points during process execution. Based on this information, we devise two approximate compliance checking methods. One of these is sound but not complete (it guarantees to find only non-compliance instances, but not to find all non-compliance instances); the other method is complete but not sound. We sketch how one can trace the state evolution back to the process activities which caused the (potential) non-compliance, and hence provide the user with some error diagnosis
Four Approaches to Supposition
The primary purpose of this paper is to shed light on the structure of four varieties of normative theories of supposition by systematically explicating the relationships between canonical representatives of each. These include qualitative and quantitative theories of indicative and subjunctive supposition. We approach this project by treating supposition as a form of 'provisional belief revision' in which a person temporarily accepts the supposition as true and makes some appropriate changes to her other opinions so as to accommodate their supposition. The idea is that suppositional judgments are supposed to reflect an agent's judgments about how things would be in some hypothetical state of affairs satisfying the supposition. Accordingly, our representative qualitative theories of indicative and subjunctive supposition are respectively based on AGM revision and KM update, while our representative quantitative ones are provided by conditionalization and imaging. We rely on a suitably adapted version of the Lockean thesis to generate qualitative judgments based on our representative quantitative theories. Ultimately, a number of new results are established that vindicate the often repeated claim that conditionalization is a probabilistic version of revision, while imaging is a probabilistic version of update
A Knowledge Compilation Map
We propose a perspective on knowledge compilation which calls for analyzing
different compilation approaches according to two key dimensions: the
succinctness of the target compilation language, and the class of queries and
transformations that the language supports in polytime. We then provide a
knowledge compilation map, which analyzes a large number of existing target
compilation languages according to their succinctness and their polytime
transformations and queries. We argue that such analysis is necessary for
placing new compilation approaches within the context of existing ones. We also
go beyond classical, flat target compilation languages based on CNF and DNF,
and consider a richer, nested class based on directed acyclic graphs (such as
OBDDs), which we show to include a relatively large number of target
compilation languages