We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, not only providing a unifying perspective of previous approaches in this area, but also demonstrating that the formalism leads to novel practical approaches to the control problem. Specifically, a natural relaxation of the dual formulation gives rise to exact iterative solutions to the finite and infinite horizon stochastic optimal control problem, while direct application of Bayesian inference methods yields instances of risk sensitive control.

We propose a method to deform robot trajectories based on affine transformations. At the heart of our approach is the concept of affine invariance: trajectories are deformed in order to avoid unexpected obstacles or to attain new goals but, at the same time, certain precise features of the original motions are preserved. Such features include for instance trajectory smoothness, periodicity, affine velocity, or more generally, all affine-invariant features, which are of particular importance in human-centered applications. Furthermore, the proposed method is very efficient and easy to implement: there is no need to re-integrate even a part of the trajectory and, in most cases, closed-form solutions can be worked out. The method is also versatile: optimization of geometric and dynamics parameters or satisfaction of inequality constraints can be taken into account in a very natural way. As illustration, we present a method for transferring human motions to humanoid robots while preserving equi-affine velocity. Building on the presented affine deformation framework, we finally revisit the concept of trajectory redundancy from the viewpoint of group theory.

Recently, large amounts of data are being published using Semantic Web standards. Simultaneously, there has been a steady rise in links between objects from multiple sources. However, the ontologies behind these sources have remained largely disconnected, thereby challenging the interoperability goal of the Semantic Web. We address this problem by automatically finding alignments between concepts from multiple linked data sources. Instead of only considering the existing concepts in each ontology, we hypothesize new composite concepts, defined using conjunctions and disjunctions of (RDF) types and value restrictions, and generate alignments between them. In addition, our techniques provide a novel method for curating the linked data web by pointing to likely incorrect or missing assertions. Our approach provides a deeper understanding of the relationships between linked data sources and increases the interoperability among previously disconnected ontologies.

The Distributed Ontology Language DOL, currently being standardized as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37, provides a unified framework for (1) ontologies formalized in heterogeneous logics, (2) modular ontologies, (3) links between ontologies, and (4) ontology annotation. A DOL ontology consists of modules formalized in languages such as OWL or Common Logic, serialized in the existing syntaxes of these languages. On top, DOL’s meta level allows for expressing heterogeneous ontologies and links between ontologies, including (heterogeneous) imports and alignments, conservative extensions, and theory interpretations. We present the abstract syntax of these meta-level constructs, with three alternative semantics: direct, translational, and collapsed semantics.

We address the issue of Ontology-Based Data Access which consists of exploiting the semantics expressed in ontologies while querying data. Ontologies are represented in the framework of existential rules, also known as Datalog+/-. We focus on the backward chaining paradigm, which involves rewriting the query (assumed to be a conjunctive query, CQ) into a set of CQs (seen as a union of CQs). The proposed algorithm accepts any set of existential rules as input and stops for so-called finite unification sets of rules (fus). The rewriting step relies on a graph notion, called a piece, which allows to identify subsets of atoms from the query that must be processed together. We first show that our rewriting method computes a minimal set of CQs when this set is finite, i.e., the set of rules is a fus. We then focus on optimizing the rewriting step. First experiments are reported in the associated technical report.

Building on recent research on preference handling in artificial intelligence and related fields, our goal is to develop a coherent and generic methodological framework for case-based reasoning (CBR) on the basis of formal concepts and methods for knowledge representation and reasoning with preferences. A preference-based approach to CBR appears to be appealing for several reasons, notably because case-based experiences naturally lend themselves to representations in terms of preference or order relations. Moreover, the flexibility and expressiveness of a preference-based formalism well accommodate the uncertain and approximate nature of case-based problem solving. In this paper, we outline the basic ideas of preference-based CBR and sketch a formal framework for realizing these ideas.

This paper proposes an approach for the adaptation of spatial or temporal cases in a case-based reasoning system. Qualitative algebras are used as spatial and temporal knowledge representation languages. The intuition behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras. A temporal example from the cooking domain is given.

Satisfiability Modulo Constraint Handling Rules (SMCHR) is the integration of the Constraint Handling Rules (CHRs) solver programming language into a Satisfiability Modulo Theories (SMT) solver framework.  Constraint solvers are implemented in CHR as a set of high-level rules that specify the simplification (rewriting) and constraint propagation behavior.  The traditional CHR execution algorithm manipulates a global store representing a flat conjunction of constraints.  This paper introduces SMCHR: a tight integration of CHR with a modern Boolean Satisfiability (SAT) solver.  Unlike CHR, SMCHR can handle (quantifier-free) formulae with an arbitrary propositional structure.  SMCHR is essentially a Satisfiability Modulo Theories (SMT) solver where the theory T is implemented in CHR.

We target the problem of accuracy and robustness in causal inference from finite data sets. Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods. We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure. These are subsequently processed in decreasing order of reliability, letting more reliable decisions take precedence in case of conflicts, until a single output model is obtained. Tests show that a basic implementation of the resulting Bayesian Constraint-based Causal Discovery (BCCD) algorithm already outperforms established procedures such as FCI and Conservative PC. It indicates which causal decisions in the output have high reliability and which do not. The approach is easily adapted to other application areas such as complex independence tests.

Learning for sentence rewriting is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referred to as string rewriting kernel, to address the problem. A string re-writing kernel measures the similarity between two pairs of strings. It can capture the lexical and structural similarity between sentence pairs without the need of constructing syntactic trees. We further propose an instance of string re-writing kernel which can be computed efficiently. Experimental results on benchmark datasets show that our method can achieve comparable results with state-of-the-art methods on two sentence re-writing learning tasks: paraphrase identification and recognizing textual entailment.