Autonomous navigation for UAVs managing motion and sensing uncertainty

We present a motion planner for the autonomous navigation of UAVs that manages motion and sensing uncertainty at planning time. By doing so, optimal paths in terms of probability of collision, traversal time and uncertainty are obtained. Moreover, our approach takes into account the real dimensions of the UAV in order to reliably estimate the probability of collision from the predicted uncertainty. The motion planner relies on a graduated fidelity state lattice and a novel multi-resolution heuristic which adapt to the obstacles in the map. This allows managing the uncertainty at planning time and yet obtaining solutions fast enough to control the UAV in real time. Experimental results show the reliability and the efficiency of our approach in different real environments and with different motion models. Finally, we also report planning results for the reconstruction of 3D scenarios, showing that with our approach the UAV can obtain a precise 3D model autonomouslyThis research was funded by the Spanish Ministry for Science, Innovation, Spain and Universities (grant TIN2017-84796-C2-1-R) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C 2018/29 and “accreditation 2016–2019, ED431G/08”). These grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Automatic generation of textual descriptions in data-to-text systems using a fuzzy temporal ontology: Application in air quality index data series

In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal references which addresses the problem of managing imprecise temporal knowledge, which is relevant in data series. We fully describe a real use case of application in the environmental information systems field, providing linguistic descriptions about the air quality index (AQI), which is a very well-known indicator provided by all meteorological agencies worldwide. We consider two different data sources of real AQI data provided by the official Galician (NW Spain) Meteorology Agency: (i) AQI distribution in the stations of the meteorological observation network and (ii) time series which describe the state and evolution of the AQI in each meteorological station. Both application models were evaluated following the current standards and good practices of manual human expert evaluation of the Natural Language Generation field. Assessment results by two experts meteorologists were very satisfactory, which empirically confirm that the proposed textual descriptions fit this type of data and service both in content and layoutThis research was funded by the Spanish Ministry for Science, Innovation and Universities (grants TIN2017-84796-C2-1-R, PID2020-112623GB-I00, and PDC2021-121072-C21) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C2018/29 and ED431G2019/04). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S

On the analysis of set-based fuzzy quantified reasoning using classical syllogistics

Syllogism is a type of deductive reasoning involving quantified statements. The syllogistic reasoning scheme in the classical Aristotelian framework involves three crisp term sets and four linguistic quantifiers, for which the main support is the linguistic properties of the quantifiers. A number of fuzzy approaches for defining an approximate syllogism have been proposed for which the main support is cardinality calculus. In this paper we analyze fuzzy syllogistic models previously described by Zadeh and Dubois et al. and compare their behavior with that of the classical Aristotelian framework to check which of the 24 classical valid syllogistic reasoning patterns or moods are particular crisp cases of these fuzzy approaches. This allows us to assess to what extent these approaches can be considered as either plausible extensions of the classical crisp syllogism or a basis for a general approach to the problem of approximate syllogism

Representation of fuzzy knowledge bases using Petri nets: operation in the truth space

In this paper the execution of Fuzzy Knowledge Bases in the truth space is briefly analyzed. The computational efficiency of the process is significantly increased by means of a parameterized description based on the linguistic truth values described by Baldwin. This permits executing the Fuzzy Knowledge Base through operations involving only simple numerical values, thus avoiding the direct analytic manipulation of possibility distributions. A Petri Net-based formalism that permits representing both the Fuzzy Knowledge Base and different dynamic processes performed onto it (execution following different strategies cycles and loops detection) is also presented. The paper focus on the algorithm for carrying out interferences in situations when information for all the input variables in the Fuzzy Knowledge Base is available

A Vector-Based Classification Approach for Remaining Time Prediction in Business Processes

In this paper, we deal with one of the current challenges in process mining enhancement: the prediction of remaining times in business processes. Accurate predictions of the remaining time, defined as the required time for an instance process to finish, are critical in many systems for organizations being able to establish a priori requirements, for optimal management of resources or for improving the quality of the services organizations provide. Our approach consists of i) extracting and assessing a number of features on the business logs, that provide a structural characterization of the traces; ii) extending the well-known annotated transition system (ATS) model to include these features; iii) proposing a partitioning strategy for the lists of features associated to each state in the extended ATS; and iv) applying a linear regression technique to each partition for predicting the remaining time of new traces. Extensive experimentation using eight attributes and ten real-life datasets show that the proposed approach outperforms in terms of mean absolute error and accuracy all the other approaches in state of the art, which includes ATS-based, non-ATS based as well as Deep Learning-based approachesThis work was supported in part by the Spanish Ministry for Science, Innovation, and Universities under Grant TIN2017-84796-C2-1-R, and in part by the Galician Ministry of Education, University, and Professional Training under Grant ED431C 2018/29 and ‘‘accreditation 2016-2019, Grant ED431G/08.’’ All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Resumen de la XI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2005)

En este informe se resumen las principales actividades y los resultados de la XI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2005), celebrada en Santiago de Compostela los días 16, 17 y 18 de Noviembre de 2005