Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel ...

In the research of public health, psychology, and social sciences, many research questions investigate the relationship between a categorical outcome variable and continuous predictor variables. The focus of this paper is to develop a model to build this relationship when both the categorical outcome and the predictor variables are latent (i.e. not observable directly). This model extends the l...

The paper considers the problem of modelling association between two exposure variables in a matched case-control study, where both the exposures may be partially missing. The exposure variables could all be categorical or continuous or could be a mixed set of some categorical and some continuous variables. Association models for the missing exposure variables using the completely observed cova...

Nonresponse is very common in survey sampling. Nonignorable nonresponse, a response mechanism in which the response probability of a survey variable Y depends directly on the value of Y regardless of whether Y is observed or not, is the most difficult type of nonresponse to handle. The population mean estimators ignoring the nonrespondents typically have heavy biases. This paper studies an empi...

The main objective of this work is to study mathematical properties of computational paths. Originally proposed by de Queiroz & Gabbay (1994) as ‘sequences or rewrites’, computational paths are taken to be terms of the identity type of Martin Löf’s Intensional Type Theory, since these paths can be seen as the grounds on which the propositional equality between two computational objects stand. F...

Rewrite theories and their associated Kripke structures constitute a flexible and executable framework in which a wide range of systems can be studied. We present a general notion of simulation between Kripke structures, study its categorical aspects, and propose rewriting logic as a framework in which these simulations can be represented. Several representability results showing that rewriting...

Simulations are a very natural way of relating concurrent systems, which are mathematically modeled by Kripke structures. The range of available notions of simulations makes it very natural to adopt a categorical viewpoint in which Kripke structures become the objects of several categories while the morphisms are obtained from the corresponding notion of simulation. Here we define in detail sev...

Many important real-world problems, such as patrol or search and rescue, could benefit from the ability to train teams of robots to coordinate. One major challenge to achieving such coordination is determining the best way for robots on such teams to communicate with each other. Typical approaches employ hand-designed communication schemes that often require significant effort to engineer. In c...

The index of dissimilarity, often denoted by Delta, is commonly used, especially in social science and with large datasets, to describe the lack of fit of models for categorical data. In this paper the definition and sampling properties of the index are investigated for general loglinear and log-multiplicative models. It is argued that in some applications a standardized version of the index is...

We develop the first statistical matching micro approach reflecting the natural uncertainty arising during the integration of categorical data. A complete synthetic file is obtained by imprecise imputation, replacing missing entries by sets of suitable values. We discuss three imprecise imputation strategies and raise ideas on potential refinements by logical constraints or likelihood-based arg...