27 research outputs found
Managing Interval Resources in Automated Planning
In this paper RDPPLan, a model for planning with quantitative resources specified as numerical
intervals, is presented. Nearly all existing models of planning with resources require to specify exact values for
updating resources modified by actions execution. In other words these models cannot deal with more
realistic situations in which the resources quantities are not completely known but are bounded by intervals.
The RDPPlan model allow to manage domains more tailored to real world, where preconditions and effects
over quantitative resources can be specified by intervals of values, in addition mixed logical/quantitative and
pure numerical goals can be posed. RDPPlan is based on non directional search over a planning graph, like
DPPlan, from which it derives, it uses propagation rules which have been appropriately extended to the
management of resource intervals. The propagation rules extended with resources must verify invariant
properties over the planning graph which have been proven by the authors and guarantee the correctness of
the approach. An implementation of the RDPPlan model is described with search strategies specifically
developed for interval resources
Using optimisation meta-heuristics for the roughness estimation problem in river flow analysis
open access articleClimate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manningâs n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ânâ. First, an objective function is designed for measuring the quality of âcandidateâ Manningâs coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers âPagliaâ and âAnieneâ. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models
Metodi computazionali per l'inferenza bayesiana con dati incompleti
Dottorato di ricerca in metodi matematici e statistici per la ricerca economica e sociale. 8. ciclo. A.a. 1994-95. Relatore G. Galmacci. Coordinatore A. ForcinaConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal
Acyclic directed graphs to represent conditional independence models
In this paper we consider conditional independence models closed under graphoid properties. We investigate their representation by means of acyclic directed graphs (DAG). A new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Finally, some properties ensuring the existence of perfect maps are provided. These conditions can be used to define a procedure able to find a perfect map for some classes of independence models. © 2009 Springer Berlin Heidelberg
Conditional independence structure and its closure: Inferential rules and algorithms
In this paper, we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements. (C) 2009 Elsevier Inc. All rights reserved
Acyclic directed graphs representing independence models
In this paper we study the problem of representing probabilistic independence models, in particular those closed under graphoid properties. We focus on acyclic directed graph (DAG): a new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Moreover, we provide a necessary and sufficient condition for the existence of a perfect map representing an independence model and we describe an algorithm based on this characterization. (C) 2010 Elsevier Inc. All rights reserved