To combine ordered data originating from multiple sources, one needs a framework that can represent uncertainty about the possible orderings or, as we call it, order-incomplete data. Examples of such data are lists of properties (such as hotels and restaurants) ranked by an unknown function reflecting relevance or customer ratings; documents edited concurrently with uncertainty on the order of ...

Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.

In this paper we explore a research direction in reasoning about actions stemming from the Robot-Tino Project at the University of Rome. We introduce a logical formalism that combines a very expressive logic of programs, the modal mu-calculus, with a special use of a minimal knowledge operator. Reasoning in such formalism can be done by integrating model checking for modal mu-calculus and propo...

Modal logics have proven to be a very successful tool for reasoning about games. However, until now, although logics have been put forward for games in both normal form and games in extensive form, and for games with complete and incomplete information, the focus in the logic community has hitherto been on games with pure strategies. This paper is a first to widen the scope to logics for games ...

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives|the likelihood-based and the Bayesian. The goal is twofold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorith...

In this paper we explore a research direction in reasoning about actions stemming from the Robot-Tino Project at the University of Rome. We introduce a logical formalism that combines a very expressive logic of programs, the modal mu-calculus, with a special use of a minimal knowledge operator. Reasoning in such formalism can be done by integrating model checking for modal mu-calculus and propo...

In this paper we develop an efficient numerical method for the finite-element model updating of damped gyroscopic systems based on incomplete complex modal measured data. It is assumed that the analytical mass and stiffness matrices are correct and only the damping and gyroscopic matrices need to be updated. By solving a constrained optimization problem, the optimal corrected symmetric damping ...

In this paper we develop an efficient numerical method for the finite-element model updating of damped gyroscopic systems based on incomplete complex modal measured data. It is assumed that the analytical mass and stiffness matrices are correct and only the damping and gyroscopic matrices need to be updated. By solving a constrained optimization problem, the optimal corrected symmetric damping ...

We consider the problem of pan-tilt sensor control for active segmentation of incomplete multi-modal data. Since demanding optimal control does not allow for online replanning, we rather employ the optimal planner offline to provide guiding samples for learning a CNN-based control policy in a guided Q-learning framework. The proposed policy initialization and guided Q-learning avoids poor local...

We propose a new definition of Representation theorem for many-valued modal logics, based on a complete latice of algebraic truth values, and define the stronger relationship between algebraic models of a given logic L and relational structures used to define the Kripke possible-world semantics for L. Such a new framework offers clear semantics for the satisfaction algebraic relation, based on ...