1,000 research outputs found
Logic Programming as Constructivism
The features of logic programming that
seem unconventional from the viewpoint of classical logic
can be explained in terms of constructivistic logic. We
motivate and propose a constructivistic proof theory of
non-Horn logic programming. Then, we apply this formalization
for establishing results of practical interest.
First, we show that 'stratification can be motivated in a
simple and intuitive way. Relying on similar motivations,
we introduce the larger classes of 'loosely stratified' and
'constructively consistent' programs. Second, we give a
formal basis for introducing quantifiers into queries and
logic programs by defining 'constructively domain
independent* formulas. Third, we extend the Generalized
Magic Sets procedure to loosely stratified and constructively
consistent programs, by relying on a 'conditional
fixpoini procedure
Relational Constraint Driven Test Case Synthesis for Web Applications
This paper proposes a relational constraint driven technique that synthesizes
test cases automatically for web applications. Using a static analysis,
servlets can be modeled as relational transducers, which manipulate backend
databases. We present a synthesis algorithm that generates a sequence of HTTP
requests for simulating a user session. The algorithm relies on backward
symbolic image computation for reaching a certain database state, given a code
coverage objective. With a slight adaptation, the technique can be used for
discovering workflow attacks on web applications.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330
Task acquisition with a description logic reasoner
In many knowledge based systems the application domain is modeled in an object-centered formalism. Research in knowledge acquisition has given evidence that this approach allows one to adequately model the conceptual structures of human experts. However, when a novice user wants to describe a particular task to be solved by such a system he has to be well acquainted with the underlying domain model, and therefore is charged with the burden of making himself familiar with it. We aim at giving automated support to a user in this process, which we call task acquisition. This paper describes the TACOS system, which guides a user through an object-centered domain model and gives support to him in specifying his task. A characteristic of TACOS is that the user can enter only information that is meaningful and consistent with the domain model. In order to identify such information, TACOS exploits the ability of a description logic based knowledge representation system to reason about such models
Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification
The Inductive Logic Programming community has considered
proof-complexity and model-complexity, but, until recently,
size-complexity has received little attention. Recently a
challenge was issued "to the international computing community"
to discover low size-complexity Prolog programs for classifying
trains. The challenge was based on a problem first proposed by
Ryszard Michalski, 20 years ago. We interpreted the challenge
as a problem in cost-sensitive classification and we applied a
recently developed cost-sensitive classifier to the competition.
Our algorithm was relatively successful (we won a prize). This
paper presents our algorithm and analyzes the results of the
competition
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