Papers by Patricia Vargas
Concurrency and Computation: Practice and Experience, 2007
One of the challenges in Grid computing research is to provide a means to automatically submit, m... more One of the challenges in Grid computing research is to provide a means to automatically submit, manage, and monitor applications whose main characteristic is to be composed of a large number of tasks. The large number of explicit tasks, generally placed on a centralized job queue, can cause several problems: (1) they can quickly exhaust the memory of the submission machine; (2) they can deteriorate the response time of the submission machine due to these demanding too many open ports to manage remote execution of each of the tasks; (3) they may cause network traffic congestion if all tasks try to transfer input and/or output files across the network at the same time; (4) they make it impossible for the user to follow execution progress without an automatic tool or interface; (5) they may depend on fault-tolerance mechanisms implemented at application level to ensure that all tasks terminate successfully. In this work we present and validate a novel architectural model, GRAND (Grid Robust ApplicatioN Deployment), whose main objective is to deal with the submission of a large numbers of tasks. Copyright © 2006 John Wiley & Sons, Ltd.
This work proposes a biologically inspired system for the coordination of multiple and possible c... more This work proposes a biologically inspired system for the coordination of multiple and possible conflicting behaviours in an autonomous mobile robot, devoted to explore novel scenarios while ensuring its internal variables dynamics. The proposed Evolutionary Artificial Homeostatic System, derived from the study of how an organism would self-regulate in order to keep its essential variables within a limited range (homeostasis), is composed of an artificial endocrine system, including two hormones and two hormone receptors, and also three previously evolved NSGasNet artificial neural networks. It is shown that the integration of receptors enhance the system robustness without incorporating to the three evolved NSGas-Nets more a priori knowledge. The experiments conducted also show that the proposed multi-hormone evolutionary artificial homeostatic system is able to successfully coordinate a multiple and conflicting behaviours task, being also robust enough to cope with internal and external disruptions.
This paper addresses the role of space in evolving a novel Non-Spatial GasNet model. It illustrat... more This paper addresses the role of space in evolving a novel Non-Spatial GasNet model. It illustrates that this particular neural network model which make use of modulatory effects of diffusing gases has its evolvability improved when its neurons are not constrained to a Euclidean space. The results show that successful behaviour is achieved in fewer evaluations for the novel unconstrained GasNet than for the original model.
The problem of minimization of energy losses in power distribution systems can be formulated as o... more The problem of minimization of energy losses in power distribution systems can be formulated as obtaining the “best” network configuration, through the manipulation of sectionalizing switches. Using graph terminology, we have a combinatorial optimization problem, whose solution corresponds to finding a minimum spanning tree for the network. As an on-line approach to loss reduction in electric power distribution networks, this paper relies on Learning Classifier Systems to continually proposed network configurations close to the one associated with minimum energy losses, in the case of timevarying profiles of energy requirement. In order to evolve the set of rules that composes the Classifier System, operators for selection, reproduction and mutation are applied. Case studies illustrate the possibilities of this approach.
Several works on grid computing have been proposed in the last years. However, most of them, incl... more Several works on grid computing have been proposed in the last years. However, most of them, including available software, can not deal properly with some issues related to control of applications that spread a very large number of tasks across the grid network. This work presents a step toward solving the problem of controlling such applications. We propose and discuss an architectural model called GRAND (Grid Robust ApplicatioN Deployment) based on partitioning and hierarchical submission and control of such applications. The main contribution of our model is to be able to control the execution of a huge number of distributed tasks while preserving data locality and reducing the load of the submit machines. We propose a taxonomy to classify application models to run in grid environments and partitioning methods. We also present our application description language GRID-ADL.
This paper proposes a non-parametric hybrid system for autonomous navigation combining the streng... more This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.
This work is the first attempt to investigate the neural dynamics of a simulated robotic agent en... more This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuramoto model of coupled oscillators as its nervous system. The main objectives are to shed new light into the role of neuronal synchronization and phase towards the generation of cognitive behaviours and to initiate an investigation on the efficacy of such systems as practical robot controllers. The first experiment is an active categorical perception task in which the robot has to discriminate between moving circles and squares. In the second task, the robotic agent has to approach moving circles with both normal and inverted vision thus adapting to both scenarios. These tasks were chosen for being considered as benchmarks in the evolutionary robotics and adaptive behaviour communities. The results obtained indicate the feasibility of the framework in the analysis and generation of embodied cognitive behaviours.
GasNet artificial neural networks can be used as complex neurocontrollers involving virtual chemi... more GasNet artificial neural networks can be used as complex neurocontrollers involving virtual chemical neuromodulation as well as synaptic interaction. The aim of this paper is to further explore the role of space in GasNet models on a delayedresponse robot task. Comparative results demonstrate that the use of spatial constraints is not a prerequisite for a good performance of the original model in terms of speed of evolution.
Many researchers are developing frameworks inspired by natural, especially biological, systems to... more Many researchers are developing frameworks inspired by natural, especially biological, systems to solve complex real-world problems. This work extends previous work in the field of biologically inspired computing, proposing an artificial endocrine system for autonomous robot navigation. Having intrinsic self-organizing behaviour, the novel artificial endocrine system can be applied to a wide range of problems, particularly those that involve decision making under changing environmental conditions, such as autonomous robot navigation. This work draws on "embodied cognitive science", including the study of intelligence, adaptivity, homeostasis, and the dynamic aspects of cognition, in order to help lay down fundamental principles and techniques for a novel approach to more biologically plausible artificial homeostatic systems. Results from using the artificial endocrine system to control a simulated robot are presented.
Holoparadigm (Holo) is a multiparadigm model oriented to development of parallel and distributed ... more Holoparadigm (Holo) is a multiparadigm model oriented to development of parallel and distributed programs. In this paper we propose the Distributed Holo (DHolo), a model to support the distributed execution of programs developed in Holo. DHolo is based on object mobility and blackboards. This distributed model can be fully implemented on Java platform. Specifically, mobility is implemented using Voyager and blackboard using Jada tuple space.
Grid environments are ideal for executing applications that require a huge amount of computationa... more Grid environments are ideal for executing applications that require a huge amount of computational work, both due to the big number of tasks to execute and to the large amount of data to be analysed. Unfortunately, current tools may require that users deal themselves with corrupted outputs or early termination of tasks. This becomes incovenient as the number of parallel runs grows to easily exceed the thousands. ReGS is a user-level software designed to provide automatic detection and restart of corrupted or early terminated tasks. ReGS uses a web interface to allow the setup and control of grid execution, and provides automatic input data setup. ReGS allows the automatic detection of job dependencies, through the GRID-ADL task management language. Our results show that besides automatically and effectively managing a huge number of tasks in grid environments, ReGS is also a good monitoring tool to spot grid nodes pitfalls.
One of the challenges in grid computing research is to provide means to automatically submit, man... more One of the challenges in grid computing research is to provide means to automatically submit, manage, and monitor applications which spread a large number of tasks. The usual way of managing these tasks is to represent each one as an explicit node in a graph, and this is the approach taken by many grid systems up to date. This approach can quickly saturate the machine where the application is launched, as we increase the number of tasks. In this work we present and validate a novel architectural model, GRAND (Grid Robust ApplicatioN Deployment), whose main objective is to deal with the problem of memory and load saturation of the submission machine. GRAND is implemented at a middleware level, aiming at providing a distributed task submission through a hierarchical organization. This paper provides an overview of the GRAND submission model as well our implementation. Initial results show that our approach can be much more effective than other approaches in the literature.
This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation o... more This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NS-GasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. The switching among the NSGasNets is implemented as an artificial endocrine system (AES), which is based on a system of coupled nonlinear difference equations. The NSGasNets are synthesized by means of an evolutionary algorithm. The obtained neuro-endocrine controller is adopted in simulated and real benchmark applications, and the additional flexibility provided by the use of NSGasNet, together with the existence of an automatic coordination system, guides to convincing levels of performance.
Abstract This work deals with the problem of scheduling jobs to identical parallel processors wit... more Abstract This work deals with the problem of scheduling jobs to identical parallel processors with the goal of minimizing the completion time of the last processor to finish its execution (makespan). This problem is known to be NP-Hard. The algorithm proposed here is inspired by the immune systems of vertebrate animals. The advantage of combinatorial optimization algorithms based on artificial immune systems is the inherent ability to preserve a diverse set of near-optimal solutions along the search. The results produced by the method are ...
The multiparadigm approach integrates programming language paradigms. We have proposed the Holopa... more The multiparadigm approach integrates programming language paradigms. We have proposed the Holoparadigm (Holo) as a multiparadigm model oriented to the development of distributed systems. Holo uses a logic blackboard (called history) to implement a coordination mechanism. The programs are organized in levels using abstract entities called beings. First, we describe the principal concepts of the Holoparadigm. After, we propose the Distributed Holo (DHolo), a model to support the distributed execution of programs developed in Holo. DHolo is based on object mobility and blackboards. This distributed model can be fully implemented on the Java platform. Experiments were done using Voyager and Horb to implement mobility. Blackboards were implemented using Java and JavaSpaces.
This work proposes a biologically inspired system for the coordination of multiple and possible c... more This work proposes a biologically inspired system for the coordination of multiple and possible conflicting behaviours in an autonomous mobile robot, devoted to explore novel scenarios while ensuring its internal variables dynamics. The proposed Evolutionary Artificial Homeostatic System, derived from the study of how an organism would self-regulate in order to keep its essential variables within a limited range (homeostasis), is composed of an artificial endocrine system, including two hormones and two hormone receptors, and also three previously evolved NSGasNet artificial neural networks. It is shown that the integration of receptors enhance the system robustness without incorporating to the three evolved NSGas-Nets more a priori knowledge. The experiments conducted also show that the proposed multi-hormone evolutionary artificial homeostatic system is able to successfully coordinate a multiple and conflicting behaviours task, being also robust enough to cope with internal and external disruptions.
This paper addresses the role of space in evolving a novel Non-Spatial GasNet model. It illustrat... more This paper addresses the role of space in evolving a novel Non-Spatial GasNet model. It illustrates that this particular neural network model which make use of modulatory effects of diffusing gases has its evolvability improved when its neurons are not constrained to a Euclidean space. The results show that successful behaviour is achieved in fewer evaluations for the novel unconstrained GasNet than for the original model.
This paper presents DAOS, a model for exploitation of And-and Or-parallelism in logic programs. D... more This paper presents DAOS, a model for exploitation of And-and Or-parallelism in logic programs. DAOS assumes a physically distributed memory environment and a logically shared address space. Exploiting both major forms of implicit parallelism should serve a broadest range of applications. Besides, a model that uses a distributed memory environment provides scalability and can be implemented over a computer network. However, distributed implementations of logic programs have to deal with communication overhead and inherent complexity of distributed memory managent. DAOS overcomes those problems through the use of a distributed shared memory layer to provide single-writer, multiple-readers sharing for the main execution stacks combined with explicit message passing for work distribution and management.
The problem of minimization of energy losses in power distribution systems can be formulated as o... more The problem of minimization of energy losses in power distribution systems can be formulated as obtaining the “best” network configuration, through the manipulation of sectionalizing switches. Using graph terminology, we have a combinatorial optimization problem, whose solution corresponds to finding a minimum spanning tree for the network. As an on-line approach to loss reduction in electric power distribution networks, this paper relies on Learning Classifier Systems to continually proposed network configurations close to the one associated with minimum energy losses, in the case of timevarying profiles of energy requirement. In order to evolve the set of rules that composes the Classifier System, operators for selection, reproduction and mutation are applied. Case studies illustrate the possibilities of this approach.
This paper presents one form of mapping Artificial Immune Systems (AIS) into Learning Classifier ... more This paper presents one form of mapping Artificial Immune Systems (AIS) into Learning Classifier Systems (LCS). Artificial Immune Systems can be defined as adaptive systems inspired by theoretical models and principles of the biological immune system and applied to solve problems in the most diverse domains, from biology to computing. Similar to Learning Classifier Systems, already used to model complex adaptive systems, a better understanding of Artificial Immune Systems can be obtained when they are analysed under the perspective of complex adaptive systems. One of the goals here is to determine complementary features of both systems (LCS and AIS), aiming at providing a novel mapping conception. The formal treatment proposed along the paper may then be used to integrate models for complex adaptive systems.
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Papers by Patricia Vargas