The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.Comment: 12 pages, and 8 figures. This works was presented in IBERAMIA 201

Efficient comparison of independence structures of log-linear models

Log-linear models are a family of probability distributions which capture a variety of relationships between variables, including context-specific independencies. There are a number of approaches for automatic learning of their independence structures from data, although to date, no efficient method exists for evaluating these approaches directly in terms of the structures of the models. The only known methods evaluate these approaches indirectly through the complete model produced, that includes not only the structure but also the model parameters, introducing potential distortions in the comparison. This work presents such a method, that is, a measure for the direct comparison of the independence structures of log-linear models, inspired by the Hamming distance comparison method used in undirected graphical models. The measure presented can be efficiently computed in terms of the number of variables of the domain, and is proven to be a distance metric

A Comparative Study of Markov Network Structure Learning Methods Over Data Streams

Abstract-Markov network is a widely used graphical representation of data in applications such as natural language and computational biology. This undirected graph consists of nodes and edges as attributes and its dependencies respectively. One major challenge in a learning task involving Markov network is to learn its structure, i.e. attribute dependencies, from data. This has been the subject of various studies in the recent past, which uses heuristics to estimate dependencies from data. In this paper, we highlight the challenges of Markov network structure learning, and review existing methods addressing these challenges. In particular, we study the scalability of these heuristics over streaming data where data instances are assumed to occur continuously. Furthermore, we propose a new heuristic based on clustering of features, consisting of attribute dependencies, that can seamlessly update the model structure as new data arrive in a stream. This clustering technique effectively reduces search space and uses fewer number of features to generate a single model. Weight learning and inference is performed at the end of each data chunk consisting of data instances arriving within a fixed time frame. We empirically evaluate the proposed heuristic by comparing the CMLL score, on various datasets (both streaming and non-streaming), with other state-of-the-art methods

Real-time optimization of working memory in autonomous reasoning for high-level control of cognitive robots deployed in dynamic environments

High-level, real-time mission control of autonomous and semi-autonomous robots, deployed in remote and dynamic environments, remains a research challenge. Robots operating in these environments require some cognitive ability, provided by a simple, but robust, cognitive architecture. The most important process in a cognitive architecture is the working memory, with core functions being memory representation, memory recall, action selection and action execution, performed by the central executive. The cognitive reasoning process uses a memory representation, based on state flows, governed by state transitions with simple, quantified propositional transition formulae. In this thesis, real-time working memory quantification and optimization is performed using a novel adaptive entropy-based fitness quantification (AEFQ) algorithm and particle swarm optimization (PSO), respectively. A cognitive architecture, using an improved set-based PSO is developed for real-time, high-level control of single-task robots and a novel coalitional games-theoretic PSO (CG-PSO) algorithm extends the cognitive architecture for real-time, high-level control in multi-task robots. The performance of the cognitive architecture is evaluated by simulation, where a UAV executesfour use cases: Firstly, for real-time high-level, single-task control: 1) relocating the UAV to a charging station and 2) collecting and delivering medical equipment. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. Secondly, for real-time high-level control of multi-task autonomous vehicle control: 3) delivering medical equipment to an incident and 4) provide aerial security surveillance support. The performance of the architecture is measured in terms of completeness and cognitive processing time and cue processing time. The results show that coalitions correctly represent optimal memory and action selection in real-time, while overall processing time is within a feasible time limit, arbitrarily set to 2 seconds in this study

Improving Markov network structure learning using decision trees

Abstract Most existing algorithms for learning Markov network structure either are limited to learning interactions among few variables or are very slow, due to the large space of possible structures. In this paper, we propose three new methods for using decision trees to learn Markov network structures. The advantage of using decision trees is that they are very fast to learn and can represent complex interactions among many variables. The first method, DTSL, learns a decision tree to predict each variable and converts each tree into a set of conjunctive features that define the Markov network structure. The second, DT-BLM, builds on DTSL by using it to initialize a search-based Markov network learning algorithm recently proposed b

Improving Markov network structure learning using decision trees

Most existing algorithms for learning Markov network structure either are limited to learning interactions among few variables or are very slow, due to the large space of possible structures. In this paper, we propose three new methods for using decision trees to learn Markov network structures. The advantage of using decision trees is that they are very fast to learn and can represent complex interactions among many variables. The first method, DTSL, learns a decision tree to predict each variable and converts each tree into a set of conjunctive features that define the Markov network structure. The second, DT-BLM, builds on DTSL by using it to initialize a search-based Markov network learning algorithm recently proposed by Davis and Domingos (2010). The third, DT+L1, combines the features learned by DTSL with those learned by an L1-regularized logistic regression method (L1) proposed by Ravikumar et al. (2009). In an extensive empirical evaluation on 20 datasets, DTSL is comparable to L1 and significantly faster and more accurate than two other baselines. DT-BLM is slower than DTSL, but obtains slightly higher accuracy. DT+L1 combines the strengths of DTSL and L1 to perform significantly better than either of them with only a modest increase in training time.status: publishe