4 research outputs found
Inductive learning of answer set programs
The goal of Inductive Logic Programming (ILP) is to find a hypothesis that
explains a set of examples in the context of some pre-existing background
knowledge. Until recently, most research on ILP targeted learning definite
logic programs. This thesis constitutes the first comprehensive work on
learning answer set programs, introducing new learning frameworks, theoretical
results on the complexity and generality of these frameworks, algorithms for
learning ASP programs, and an extensive evaluation of these algorithms.
Although there is previous work on learning ASP programs, existing learning
frameworks are either brave -- where examples should be explained by at
least one answer set -- or cautious where examples should be explained
by all answer sets. There are cases where brave induction is too weak and
cautious induction is too strong. Our proposed frameworks combine brave and
cautious learning and can learn ASP programs containing choice rules and
constraints. Many applications of ASP use weak constraints to express a
preference ordering over the answer sets of a program. Learning weak
constraints corresponds to preference learning, which we achieve by
introducing ordering examples. We then explore the generality of our
frameworks, investigating what it means for a framework to be general enough to
distinguish one hypothesis from another. We show that our frameworks are more
general than both brave and cautious induction.
We also present a new family of algorithms, called ILASP (Inductive Learning of
Answer Set Programs), which we prove to be sound and complete. This work
concerns learning from both non-noisy and noisy examples. In the latter case,
ILASP returns a hypothesis that maximises the coverage of examples while
minimising the length of the hypothesis. In our evaluation, we show that ILASP
scales to tasks with large numbers of examples finding accurate hypotheses
even in the presence of high proportions of noisy examples.Open Acces
Learning non-monotonic Logic Programs to Reason about Actions and Change
[Resumen]
El objetivo de esta tesis es el dise帽o de m茅todos de aprendizaje autom谩tico capaces de encontrar un modelo de un sistema din谩mico que determina c贸mo las propiedades del sistema con afectadas por la ejecuci贸n de acciones, Esto permite obtener de manera autom谩tica el conocimiento espec铆fico del dominio necesario para las tareas de planficaci贸n o diagn贸stico as铆 como predecir el comportamiento futuro del sistema.
La aproximaci贸n seguida difiere de las aproximaciones previas en dos aspectos.
Primero, el uso de formalismos no mon贸tonos para el razonamiento sobre acciones y el cambio con respecto a los cl谩sicos operadores tipo STRIPS o aquellos basados en formalismos especializados en tareas muy concretas, y por otro lado el uso de m茅todos de aprendizaje de programas l贸gicos (Inductive Logic Programming). La combinaci贸n de estos dos campos permite obtener un marco declarativo para el aprendizaje, donde la especificaci贸n de las acciones y sus efectos es muy intuitiva y natural y que permite aprender teor铆as m谩s expresivas que en anteriores aproximaciones