210 research outputs found
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
05051 Abstracts Collection -- Probabilistic, Logical and Relational Learning - Towards a Synthesis
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Introduction to the special issue on probability, logic and learning
Recently, the combination of probability, logic and learning has received considerable attention in the artificial intelligence and machine learning communities; see e.g. Getoor and Taskar (2007); De Raedt et al. (2008). Computational logic often plays a major role in these developments since it forms the theoretical backbone for much of the work in probabilistic programming and logical and relational learning. Contemporary work in this area is often application- and experiment-driven, but is also concerned with the theoretical foundations of formalisms and inference procedures and with advanced implementation technology that scales well
Learning to Understand by Evolving Theories
In this paper, we describe an approach that enables an autonomous system to
infer the semantics of a command (i.e. a symbol sequence representing an
action) in terms of the relations between changes in the observations and the
action instances. We present a method of how to induce a theory (i.e. a
semantic description) of the meaning of a command in terms of a minimal set of
background knowledge. The only thing we have is a sequence of observations from
which we extract what kinds of effects were caused by performing the command.
This way, we yield a description of the semantics of the action and, hence, a
definition.Comment: KRR Workshop at ICLP 201
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
We aim to reduce the burden of programming and deploying autonomous systems
to work in concert with people in time-critical domains, such as military field
operations and disaster response. Deployment plans for these operations are
frequently negotiated on-the-fly by teams of human planners. A human operator
then translates the agreed upon plan into machine instructions for the robots.
We present an algorithm that reduces this translation burden by inferring the
final plan from a processed form of the human team's planning conversation. Our
approach combines probabilistic generative modeling with logical plan
validation used to compute a highly structured prior over possible plans. This
hybrid approach enables us to overcome the challenge of performing inference
over the large solution space with only a small amount of noisy data from the
team planning session. We validate the algorithm through human subject
experimentation and show we are able to infer a human team's final plan with
83% accuracy on average. We also describe a robot demonstration in which two
people plan and execute a first-response collaborative task with a PR2 robot.
To the best of our knowledge, this is the first work that integrates a logical
planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on
Artificial Intelligence (AAAI-13
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