Manuel Mucientes

Automatic Web Service Composition with a Heuristic-Based Search Algorithm

Service Oriented Architectures and web service technology are becoming popular in recent years. As more web services can be used over the Internet, the need to find efficient algorithms for web services composition that can deal with large amounts of services becomes important. These algorithms must deal with different issues like performance, semantics or user restrictions. In this paper we present an A* algorithm which solves the problem of semantic input-output message structure matching for web service composition. Given are quest, a service dependency graph with a subset of the original services from an external repository is dynamically generated. Then, the A*search algorithm is used to find a minimal composition that satisfies the user request. Moreover, in order to improve the performance, a set of dynamic optimization techniques has been implemented over the search process. A full experimental validation with eight different public repositories has been done showing a good performance as in all tests as the algorithm finds a valid solution with minimal number of services and execution path.

Design of a fuzzy controller in mobile robotics using genetic algorithms

The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot is described. The algorithm is based on the Iterative Rule Learning (IRL) approach, and a parameter (@d) is defined with the aim of selecting the relation between the number of rules and the quality and accuracy of the controller. The designer has to define the universe of discourse and the precision of each variable, and also the scoring function. No restrictions are placed neither in the number of linguistic labels nor in the values that define the membership functions.

Quick Design of Fuzzy Controllers With Good Interpretability in Mobile Robotics

This paper presents a methodology for the design of fuzzy controllers with good interpretability in mobile robotics. It is composed of a technique to automatically generate a training data set plus an efficient algorithm to learn fuzzy controllers. The proposed approach obtains a highly interpretable knowledge base in a very reduced time, and the designer only has to define the number of membership functions and the universe of discourse of each variable, together with a scoring function. In addition, the learned fuzzy controllers are general because the training set is composed of a number of automatically generated examples that cover the universe of discourse of each variable uniformly and with a predefined precision. The methodology has been applied to the design of a wall-following and moving object following behavior. Several tests in simulated environments using the Nomad 200 robot software and a comparison with another learning method show the performance and advantages of the proposed approach.

Multiple Hypothesis Tracking of Clusters of People

Mobile robots operating in populated environments typically can improve their service and navigation behavior when they know where people are in their vicinity and in which direction they are heading. In this paper we present an algorithm for tracking clusters of people using multiple hypothesis tracking (MHT). The motivation for our approach is that tracking clusters of objects instead of the individual objects enhances the reliability and robustness of the tracking especially when the objects move in groups. To efficiently keep track of multiple objects and clusters, our approach uses MHT in combination with Murtys algorithm. The set of hypothesis for each iteration is constructed in two consecutive steps: one for solving the data association problem, taking also into account the frequent occlusions between the objects, and the second one for considering the joining of different clusters. Our approach has been implemented and tested on a real robot and in a typical hallway environment. Experimental results demonstrate that our approach can robustly deal with several groups of people and is able to reliably manage the splits and joins of clusters

Composition of web services through genetic programming

Web Services are interfaces that describe a collection of operations that are network-accessible through standardized web protocols. When a required operation is not found, several services can be compounded to get a composite service that performs the desired task. To find this composite service a search process in a, generally, huge search space must be performed. The algorithm that composes the services must select the adequate atomic processes and, also, must choose the correct way to combine them using the different available control structures. In this paper a genetic programming algorithm for web services composition is presented. The algorithm has a context-free grammar to generate the valid structures of the composite services and, also, it includes a method to update the attributes of each node. Moreover, the proposal tries to minimize the number of services, and looks for compositions with the minimum execution path. A full experimental validation with four different repositories with up to 1,090 web services has been done, showing a great performance in all the tests as the algorithm finds a valid solution with a short execution path.

An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the web.

A fuzzy temporal rule-based velocity controller for mobile robotics

This paper describes a velocity controller implemented on a Nomad 200 mobile robot. The controller has been developed for wall-following behaviour, and its design is modularized into two blocks: angular and linear velocity control. A simple design and implementation was made for the former, with the aim of focusing the design efforts on the linear velocity control block, in order to remark the usefulness of this task. The latter has been implemented using an explicit model for knowledge representation and reasoning called fuzzy temporal rules (FTRs). This model enables to explicitly incorporate time as a variable, due to which the evolution of variables in a temporal reference can be described. Using this mechanism we obtain linear velocity values that are adapted to each different circumstance, and thus a higher average velocity as well as smoother and more robust behaviours are achieved.

Fuzzy temporal rules for mobile robot guidance in dynamic environments

The paper describes a fuzzy control system for the avoidance of moving objects by a robot. The objects move with no type of restriction, varying their velocity and making turns. Due to the complex nature of this movement, it is necessary to realize temporal reasoning with the aim of estimating the trend of the moving object. A new paradigm of fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is used for this control task. The control system has over 117 rules, which reflects the complexity of the problem to be tackled. The controller has been subjected to an exhaustive validation process and examples are shown of the results obtained.

ProDiGen: mining complete, precise and minimal structure process models with a genetic algorithm

Abstract Process discovery techniques automatically extract the real workflow of a process by analyzing the events that are collected and stored in log files. Although in the last years several process discovery algorithms have been presented, none of them guarantees to find complete, precise and simple models for all the given logs. In this paper we address the problem of process discovery through a genetic algorithm with a new fitness function that takes into account both completeness, precision and simplicity. ProDiGen (Process Discovery through a Genetic algorithm) includes new definitions for precision and simplicity, and specific crossover and mutation operators. The proposal has been validated with 39 process models and several noise levels, giving a total of 111 different logs. We have compared our approach with the state of the art algorithms; non-parametric statistical tests show that our algorithm outperforms the other approaches, and that the difference is statistically significant.

STAC: A web platform for the comparison of algorithms using statistical tests

One of the most suited techniques for comparing results obtained from computational intelligence algorithms is the statistical hypothesis testing. This method can be used to contrast if the difference between the algorithm with the best results and other algorithms is actually significant. In this paper, we present STAC (Statistical Tests for Algorithms Comparison), a new platform for statistical analysis to verify the results obtained from computational intelligence algorithms. STAC consists of three different layers for performing statistical tests: a Python library, a set of web services and a web client. We show several use cases, in which both non-expert and expert users interact with the web client and use the web services in different programming languages.