16,913 research outputs found
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter
learning of parameterized logic programs, i.e. definite clause programs
containing probabilistic facts with a parameterized distribution. It extends
the traditional least Herbrand model semantics in logic programming to
distribution semantics, possible world semantics with a probability
distribution which is unconditionally applicable to arbitrary logic programs
including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM
algorithm, the graphical EM algorithm, that runs for a class of parameterized
logic programs representing sequential decision processes where each decision
is exclusive and independent. It runs on a new data structure called support
graphs describing the logical relationship between observations and their
explanations, and learns parameters by computing inside and outside probability
generalized for logic programs. The complexity analysis shows that when
combined with OLDT search for all explanations for observations, the graphical
EM algorithm, despite its generality, has the same time complexity as existing
EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside
algorithm for PCFGs, and the one for singly connected Bayesian networks that
have been developed independently in each research field. Learning experiments
with PCFGs using two corpora of moderate size indicate that the graphical EM
algorithm can significantly outperform the Inside-Outside algorithm
Probabilistic Methodology and Techniques for Artefact Conception and Development
The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology
and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art
Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search
Universal induction relies on some general search procedure that is doomed to
be inefficient. One possibility to achieve both generality and efficiency is to
specialize this procedure w.r.t. any given narrow task. However, complete
specialization that implies direct mapping from the task parameters to
solutions (discriminative models) without search is not always possible. In
this paper, partial specialization of general search is considered in the form
of genetic algorithms (GAs) with a specialized crossover operator. We perform a
feasibility study of this idea implementing such an operator in the form of a
deep feedforward neural network. GAs with trainable crossover operators are
compared with the result of complete specialization, which is also represented
as a deep neural network. Experimental results show that specialized GAs can be
more efficient than both general GAs and discriminative models.Comment: AGI 2017 procedding, The final publication is available at
link.springer.co
- …