Catálogo de publicaciones - libros

Macro-operators are sequences of actions that can guide a planner to achieve its goals faster by avoiding search for those sequences. However, using macro-operators will also increase the branching factor of choosing operators, and as a result making planning more complex and less efficient. On the other hand, the detection and exploitation of symmetric structures in planning problems can reduce the search space by directing the search process. In this paper, we present a new method for detecting symmetric objects through subgraph-isomorphism, and exploiting the extracted information in macro-operator selection. The method has been incorporated into HSP2, and tested on a collection of different planning domains.

This paper describes a novel approach will be called guided hill climbing to improve the efficiency of hill climbing in the planning domains. Unlike simple hill climbing, which evaluates the successor states without any particular order, guided hill climbing evaluates states according to an order recommended by an auxiliary guiding heuristic function. Guiding heuristic function is a self-adaptive and cost effective function based on the main heuristic function of hill climbing. To improve the performance of the method in various domains, we defined several heuristic functions and created a mechanism to choose appropriate functions for each particular domain. We applied the guiding method to the enforced hill climbing, which has been used by the Fast Forward planning system (FF). The results show a significant improvement in the efficiency of FF in a number of domains.

The present paper approaches the loading distribution of trucks for Product Transportation as a rich problem. This is formulated with the classic Bin Packing Problem and five variants associated with a real case of study. A state of the art review reveals that related work deals with three variants at the most. Besides, they do not consider its relation with the vehicle routing problem. For the solution of this new rich problem a heuristic-deterministic algorithm was developed. It works together with a metaheuristic algorithm to assign routes and loads. The results of solving a set of real world instances show an average saving of three vehicles regarding their manual solution; this last needed 180 minutes in order to solve an instance and the actual methodology takes two minutes. On average, the demand was satisfied in 97.45%. As future work the use of a non deterministic algorithm is intended.

Probabilistic Roadmap approaches (PRMs) have been successfully applied in motion planning of robots with many degrees of freedom. In recent years, the community has proposed deterministic sampling as a way to improve the performance in these planners. However, our recent results show that the choice of the sampling source pseudo-random or deterministic- has small impact on a PRM planner’s performance. We used two single-query PRM planners for this comparative study. The advantage of the deterministic sampling on the pseudo-random sampling is only observable in low dimension problems. The results were surprising in the sense that deterministic sampling performed differently than claimed by the designers.

This research presents a novel approach to solve m-machine no-wait flowshop scheduling problem. A continuous flowshop problem with total flowtime as criterion is considered applying a hybrid evolutionary algorithm. The performance of the proposed method is evaluated and the results are compared with the best known in the literature. Experimental tests show the superiority of the evolutionary hybrid regarding the solution quality.

A new approach is proposed to tackle integrated decision making associated to supply chains. This procedure enables reliable decisions concerning the set of order demands along a supply chain. This is accomplished by means of supply chain scheduling simulations, based on the use of Constraint Programming. The definition of time windows for all tasks poses as an indication that no infeasibility was found during supply chain analysis. Scheduling of orders along the supply chain is treated as a constraint satisfaction problem. It suffices to identify any feasible schedule to state that simulated decisions are acceptable. The main contribution of this work is the integration of Constraint Programming concepts within a decision-support system to support supply chain decisions.

Since DNA-dimer analysis has demonstrated to provide a very conserved pattern that has been suggested as a genome signature, in this paper we present a computational study of DNA-dimer distribution in a collection of Retroviral genomes. This analysis is based on two main steps: the generation of the target dataset, in this step, the DNA-dimer distribution variables are calculated and transformed to categorical data using Fuzzy Sets. And the induction of a Bayesian Network from the dataset. This induction technique is based on Genetic Algorithms. We have found interesting causal relationships between the DNA-dimer distribution variables and a set of chemical variables. These results could provide new directions in future Retroviral genomic investigations. The computational methodology presented in this paper has demonstrated to be an interesting tool for the study and the analysis of genomic sequences.

The purpose of this paper is to present in an organized form the concept of cancer detection based on data obtained from SELDI-TOF-MS. In this paper, we outline the full process of detection: from raw data, through pre-processing towards classification. Methods and algorithms, their characteristics and suggested implementation indications are described. We aim to present the over current research. Additionally, we introduce an idea of 24h/day distributed work organization and suggest how to make the research process faster.

In this paper, we propose a method for 3D (three-dimensional) modeling of individual vessels based on matching of adaptive control points to help accurately locate a disease such as arteriosclerosis. The proposed method consists of two steps: matching of corresponding control points between standard and individual vessels model, and transformation of standard vessels model. In the first step, control points are adaptively interpolated in the corresponding standard vessels image in proportion to the distance ratio if there were control points between two corner points in an individual vessels model. And then, the control points of corresponding individual vessels model matches with those of standard vessels model. In the second step, the TPS (Thin Plate Spline) interpolation function is used to modify the standard into the individual vessels model. In the experiments, we used patient angiograms from the coronary angiography in Sanggye Paik Hospital.

In this paper, the methods of the modeling and decomposition of the large and complex discrete event manufacturing systems are considered, and a methodology is presented for hierarchical and distributed control, where the cooperation of each controller is implemented so that the behavior of the overall system is not deteriorated and the task specification is completely satisfied. First, the task specification is defined as a Petrinet model at the conceptual level, and then transformed to the detailed Petrinet representation of manufacturing processes. Finally, the overall Petrinet is decomposed and the constituent subnets are assigned to the machine controllers. The machine controllers are coordinated so that the decomposed transitions fire at the same time. System coordination through communication between the coordinator and machine controllers, is presented. Modeling and control of large and complex manufacturing systems can be performed consistently using Petrinets.