In constraint solving, a critical bottleneck is the formulation of aneffective constraint model of an input problem. The Conjure system describedin this paper, a substantial step forward over prototype versions of Conjurepreviously reported, makes a valuable contribution to the automation ofconstraint modelling by automatically producing constraint models from theirspecifications in the abstract constraint specification language Essence. Aset of rules is used to refine an abstract specification into a concreteconstraint model. We demonstrate that this set of rules is readily extensibleto increase the space of possible constraint models Conjure can produce. Ourempirical results confirm that Conjure can reproduce successfully the kernelsof the constraint models of 32 benchmark problems found in the literature.

A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number ofclusters in mixture models or the number of factors in factor analysis. In this tutorial we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.

Aggregate factors (that is, those based on aggregate functions such as SUM, AVERAGE, AND etc.) in probabilistic relational models can compactly represent dependencies among a large number of relational random variables. However, propositional inference on a factor aggregating n k -valued random variables into an r -valued result random variable is O ( r k 2 n ). Lifted methods can ameliorate this to O ( r n k ) in general and O ( r k log n ) for commutative associative aggregators. In this paper, we propose (a) an exact solution constant in n when k  = 2 for certain aggregate operations such as AND, OR and SUM, and (b) a close approximation for inference with aggregate factors with time complexity constant in n . This approximate inference involves an analytical solution for some operations when k > 2. The approximation is based on the fact that the typically used aggregate functions can be represented by linear constraints in the standard ( k  –1)-simplex in R k where k is the number of possible values for random variables. This includes even aggregate functions that are commutative but not associative (e.g., the MODE operator that chooses the most frequent value). Our algorithm takes polynomial time in k (which is only 2 for binary variables) regardless of r and n, and the error decreases as n increases. Therefore, for most applications (in which a close approximation suffices) our algorithm is a much more efficient solution than existing algorithms. We present experimental results supporting these claims. We also present a (c) third contribution which further optimizes aggregations over multiple groups of random variables with distinct distributions.

In applications including chemoinformatics, bioinfor- matics, information retrieval, text classification, com- puter vision and others, a variety of common issues have been identified involving frequency of occurrence, variation and similarities of instances, and lack of pre- cise class labels. These issues continue to be important hurdles in machine intelligence and my doctoral thesis focuses on developing robust machine learning models that address the same.

The design and implementation of a robotic chess agent is described. Shallow Blue, a competitor in the AAAI 2011 Small Scale Manipulation Challenge, is constructed with low-cost components including Lego NXT bricks and is programmed using Java and Lejos.

The initial idea of using Lego Mindstorms Robots for student courses had soon to be expanded to a simulation environment as the user base in students grew larger and the need for parallel development and testing arose. An easy to use and easy to set up means of providing positioning data led to the creation of an indoor positioning system so that new users can adapt quickly and successfully, as sensors on the actual robots are difficult to configure and hard to interpret in an environmental context. A global positioning system shared among robots can make local sensors obsolete and still deliver more precise information than currently available sensors, also providing the base necessary for the robots to effectively work on shared tasks as a group. Further more, a simulator for robots programmed with Fujaba and Java which was developed along the way can be used by many developers simultaneously and lets them evaluate their code in a simple way, while close to real-world results.

In this paper, we present the exact online updating formulae for the generalized inverse of centered matrices. The computational cost is O ( mn ) for matrices of size m × n . Experimental results validate the proposed method’s accuracy and efficiency.  

We present an algorithm for compressing pattern databases (PDBs) and a method for fast random access of these com-pressed PDBs. We demonstrate the effectiveness of our technique by compressing two 6-tile sliding-tile PDBs by a factor of 12 and a 7-tile sliding-tile PDB by a factor of 24.

We present a computational method to generate explanations to answers of complex queries over biomedical ontologies and databases, using the high-level representation and efficient automated reasoners of Answer Set Programming. We show the applicability of our approach with some queries related to drug discovery over PHARMGKB, DRUGBANK, BIOGRID, CTD and SIDER.

This paper describes an approach to solving insertion tasks by a robot that uses exploratory behaviors and proprioceptive feedback. The approach was inspired by the developmental progression of insertion abilities in both chimpanzees and humans (Hayashi et al. 2006). Before mastering insertions, the infants of the two species undergo a stage where they only press objects against other objects without releasing them. Our goal was to emulate this developmental stage on a robot to see if it may lead to simpler representations for insertion tasks. Experiments were performed using a shapesorter puzzle with three different blocks and holes.