Many machine learning applications that involve relational databases incorporate first-order logic and probability. Markov Logic Networks (MLNs) are a prominent statistical relational model that consist of weighted first order clauses. Many of the current state-of-the-art algorithms for learning MLNs have focused on relatively small datasets with few descriptive attributes, where predicates are mostly binary and the main task is usually prediction of links between entities. This paper addresses what is in a sense a complementary problem: learning the structure of an MLN that models the distribution of discrete descriptive attributes on medium to large datasets, given the links between entities in a relational database. Descriptive attributes are usually nonbinary and can be very informative, but they increase the search space of possible candidate clauses. We present an efficient new algorithm for learning a directed relational model (parametrized Bayes net), which produces an MLN structure via a standard moralization procedure for converting directed models to undirected models. Learning MLN structure in this way is 200-1000 times faster and scores substantially higher in predictive accuracy than benchmark algorithms on three relational databases.

The objective of my doctoral research is bring together two fields: partially-observable reinforcement learning (PORL) and non-parametric Bayesian statistics (NPB) to address issues of statistical modeling and decision-making in complex, real-world domains.

Markov logic networks (MLNs) use first-order formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we present LSM, the first MLN structure learner capable of efficiently and accurately learning long clauses. LSM is based on the observation that relational data typically contains patterns that are variations of the same structural motifs. By constraining the search for clauses to occur within motifs, LSM can greatly speed up the search and thereby reduce the cost of finding long clauses. LSM uses random walks to identify densely connected objects in data, and groups them and their associated relations into a motif. Our experiments on three real-world datasets show that our approach is 2-5 orders of magnitude faster than the state-of-the-art ones, while achieving the same or better predictive performance.

We consider the problem of discrete multi-label entity classification in images. We argue that the framework of Markov Logic can provide a unified, well-grounded mechanism to incorporate arbitrary logical relationships between entities to improve classification in images, and thus generalizes much of the recent work on exploiting local and global context in object recognition and scene understanding. Furthermore, we show that Markov Logic can provide a powerful new set of contexts that can relate entities across images in a database for joint classification of all entities in a test set simultaneously. We relate this collective classification of images to graph-based semi-supervised learning approaches, and show that Markov Logic can effectively provide a method to unify context-related work with semi-supervised approaches in a way that neither techniques could easily do on their own. Finally, we show the efficacy of these techniques on a face recognition task on three datasets showing that adding contextual relations dramatically improves accuracy over semi-supervised learning approaches alone.

Learning, which aims at combining probabilistic graphical Markov networks have proved to be a very useful tool to models with first order logics representations. The represent probability distributions over large domains (see work that we present in this paper has been motivated by for instance, Chapter 8 in (Bishop 2006)). A Markov Network Markov Logic Networks (MLN), introduced in (Richardson is an undirected graphical model, where variables are and Domingos 2006). A Markov Logic Network is defined represented by nodes and features on subsets of variables by a set of weighted first-order formulas.

The architectural approach to AI focuses on the fixed structure underlying intelligence. Applying it to statistical relational AI should further stimulate the application of statistical relational techniques across AI, while focusing research on their commonalities, (in)compatibilities and integration. It could also yield new architectures that are simpler yet more comprehensive than today’s best.

Learning the relational structure of a domain is a fundamental problem in statistical relational learning. The deep transfer algorithm of Davis and Domingos attempts to improve structure learning in Markov logic networks by harnessing the power of transfer learning, using the second-order structural regularities of a source domain to bias the structure search process in a target domain. We propose that the clique-scoring process which discovers these second-order regularities constitutes a novel standalone method for learning the structure of Markov logic networks, and that this fact, rather than the transfer of structural knowledge across domains, accounts for much of the performance benefit observed via the deep transfer process. This claim is supported by experiments in which we find that clique scoring within a single domain often produces results equaling or surpassing the performance of deep transfer incorporating external knowledge, and also by explicit algorithmic similarities between deep transfer and other structure learning techniques.

A new method is proposed for compiling causal independencies into Markov logic networks. A Markov logic network can be viewed as compactly representing a factorization of a joint probability into the multiplication of a set of factors guided by logical formulas. We present a notion of causal independence that enables one to further factorize the factors into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The causal independence lets us specify the factor in terms of weighted, directed clauses and an associative and commutative operator, such as "or", "sum" or "max", on the contribution of the variables involved in the factors, hence combining both undirected and directed knowledge.

We argue that probabilistic programming with undirected models, in order to scale up, needs to open up. That is, instead of focusing on minimal sets of generic building blocks such as universal quantification or logical connectives, languages should grow to include specific building blocks for as many uses cases as necessary. This can not only lead to more concise models, but also to more efficient inference if we use methods that can exploit building-block specific sub-routines. As embodiment of this paradigm we present , a platform for implementing probabilistic programming languages that grow.

The starting point is an ongoing effort to structure underlying intelligent behavior, whether intended reconstruct cognitive architectures from the ground up via as models of human intelligence and/or implementations of graphical models (Koller and Friedman 2009), with the artificial intelligence (Langley, Laird and Rogers 2009). A aim of understanding existing architectures better, basic cognitive architecture may comprise memories, exploring the overall space of architectures, and decision algorithms, learning mechanisms, and some developing new and improved architectures (Rosenbloom means of interacting with external environments.