Electronic Proceedings in Theoretical Computer Science, EPTCS | Year: 2014
The paper relates two variants of semanticmodels for natural language, logical functionalmodels and compositional distributional vector space models, by transferring the logic and reasoning from the logical to the distributional models. The geometrical operations of quantum logic are reformulated as algebraic operations on vectors. A map from functional models to vector space models makes it possible to compare the meaning of sentences word by word. © Anne Preller.
Bibuli M.,CNR Institute of Neuroscience |
Caccia M.,National Research Council Italy |
Lapierre L.,LIRMM |
Bruzzone G.,National Research Council Italy
IEEE Robotics and Automation Magazine | Year: 2012
Virtual target-based path-following techniques are extended to execute the task of vehicle following in the case of unmanned surface vehicles (USVs). Indeed, vehicle following is reduced to the problem of tracking a virtual target moving at a desired range from a master vessel, while separating the spatial and temporal constraints, giving priority to the former one. The proposed approach is validated experimentally in a harbor area with the help of the prototype USVs ALANIS and Charlie, developed by Consiglio Nazionale delle Ricerche-Istituto di Studi sui Sistemi Intelligenti per lAutomazione (CNR-ISSIA). © 2012 IEEE.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2011
In this paper, we explore the combined use of inertial sensors and the Kinect for applications on rehabilitation robotics and assistive devices. In view of the deficiencies of each individual system, a new method based on Kalman filtering was developed in order to perform online calibration of sensor errors automatically whenever measurements from Kinect are available. The method was evaluated on experiments involving healthy subjects performing multiple DOF tasks.
Koriche F.,LIRMM |
Zanuttini B.,University of Caen Lower Normandy
Artificial Intelligence | Year: 2010
Conditional preference networks (CP-nets) have recently emerged as a popular language capable of representing ordinal preference relations in a compact and structured manner. In this paper, we investigate the problem of learning CP-nets in the well-known model of exact identification with equivalence and membership queries. The goal is to identify a target preference ordering with a binary-valued CP-net by interacting with the user through a small number of queries. Each example supplied by the user or the learner is a preference statement on a pair of outcomes. In this model, we show that acyclic CP-nets are not learnable with equivalence queries alone, even if the examples are restricted to swaps for which dominance testing takes linear time. By contrast, acyclic CP-nets are what is called attribute-efficiently learnable when both equivalence queries and membership queries are available: we indeed provide a learning algorithm whose query complexity is linear in the description size of the target concept, but only logarithmic in the total number of attributes. Interestingly, similar properties are derived for tree-structured CP-nets in the presence of arbitrary examples. Our learning algorithms are shown to be quasi-optimal by deriving lower bounds on the VC-dimension of CP-nets. In a nutshell, our results reveal that active queries are required for efficiently learning CP-nets in large multi-attribute domains. © 2010 Elsevier B.V. All rights reserved.
Hadjali A.,University of Rennes 1 |
Kaci S.,LIRMM |
Prade H.,University Paul Sabatier
Annals of Mathematics and Artificial Intelligence | Year: 2011
The paper presents a new approach to database preference queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. In this paper, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries. © 2012 Springer Science+Business Media B.V.