82,441 research outputs found
Learning Weak Constraints in Answer Set Programming
This paper contributes to the area of inductive logic programming by
presenting a new learning framework that allows the learning of weak
constraints in Answer Set Programming (ASP). The framework, called Learning
from Ordered Answer Sets, generalises our previous work on learning ASP
programs without weak constraints, by considering a new notion of examples as
ordered pairs of partial answer sets that exemplify which answer sets of a
learned hypothesis (together with a given background knowledge) are preferred
to others. In this new learning task inductive solutions are searched within a
hypothesis space of normal rules, choice rules, and hard and weak constraints.
We propose a new algorithm, ILASP2, which is sound and complete with respect to
our new learning framework. We investigate its applicability to learning
preferences in an interview scheduling problem and also demonstrate that when
restricted to the task of learning ASP programs without weak constraints,
ILASP2 can be much more efficient than our previously proposed system.Comment: To appear in Theory and Practice of Logic Programming (TPLP),
Proceedings of ICLP 201
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
On the Relationships Among Probabilistic Extensions of Answer Set Semantics
abstract: Answer Set Programming (ASP) is one of the main formalisms in Knowledge Representation (KR) that is being widely applied in a large number of applications. While ASP is effective on Boolean decision problems, it has difficulty in expressing quantitative uncertainty and probability in a natural way.
Logic Programs under the answer set semantics and Markov Logic Network (LPMLN) is a recent extension of answer set programs to overcome the limitation of the deterministic nature of ASP by adopting the log-linear weight scheme of Markov Logic. This thesis investigates the relationships between LPMLN and two other extensions of ASP: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. The studied relationships show how different extensions of answer set programs are related to each other, and how they are related to formalisms in Statistical Relational Learning, such as Problog and MLN, which have shown to be closely related to LPMLN. The studied relationships compare the properties of the involved languages and provide ways to compute one language using an implementation of another language.
This thesis first presents a translation of LPMLN into programs with weak constraints. The translation allows for computing the most probable stable models (i.e., MAP estimates) or probability distribution in LPMLN programs using standard ASP solvers so that the well-developed techniques in ASP can be utilized. This result can be extended to other formalisms, such as Markov Logic, ProbLog, and Pearl’s Causal Models, that are shown to be translatable into LPMLN.
This thesis also presents a translation of P-log into LPMLN. The translation tells how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LPMLN, which yields a way to compute P-log using standard ASP solvers or MLN solvers.Dissertation/ThesisMasters Thesis Computer Science 201
Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.Comment: Accepted for the machine learning journa
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