1,564 research outputs found
Reactive with tags classifier system applied to real robot navigation
7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules
VADA: A transformation-based system for variable dependence analysis
Variable dependence is an analysis problem in which the aim is to determine the set of input variables that can affect the values stored in a chosen set of intermediate program variables. This paper shows the relationship between the variable dependence analysis problem and slicing and describes VADA, a system that implements variable dependence analysis. In order to cover the full range of C constructs and features, a transformation to a core language is employed Thus, the full analysis is required only for the core language, which is relatively simple. This reduces the overall effort required for dependency analysis. The transformations used need preserve only the variable dependence relation, and therefore need not be meaning preserving in the traditional sense. The paper describes how this relaxed meaning further simplifies the transformation phase of the approach. Finally, the results of an empirical study into the performance of the system are presented
k-PathA: k-shortest Path Algorithm
One important aspect of computational systems biology
includes the identification and analysis of functional response
networks within large biochemical networks. These functional
response networks represent the response of a biological system
under a particular experimental condition which can be used to
pinpoint critical biological processes.
For this purpose, we have developed a novel algorithm to calculate
response networks as scored/weighted sub-graphs spanned by
k-shortest simple (loop free) paths. The k-shortest simple path
algorithm is based on a forward/backward chaining approach
synchronized between pairs of processors. The algorithm scales
linear with the number of processors used. The algorithm
implementation is using a Linux cluster platform, MPI lam
and mpiJava messaging as well as the Java language for the
application.
The algorithm is performed on a hybrid human network consisting
of 45,041 nodes and 438,567 interactions together with
gene expression information obtained from human cell-lines
infected by influenza virus. Its response networks show the early
innate immune response and virus triggered processes within
human epithelial cells. Especially under the imminent threat of
a pandemic caused by novel influenza strains, such as the current
H1N1 strain, these analyses are crucial for a comprehensive
understanding of molecular processes during early phases of
infection. Such a systems level understanding may aid in the
identification of therapeutic markers and in drug development
for diagnosis and finally prevention of a potentially dangerous
disease
An enhanced classifier system for autonomous robot navigation in dynamic environments
In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad
Evolving a Behavioral Repertoire for a Walking Robot
Numerous algorithms have been proposed to allow legged robots to learn to
walk. However, the vast majority of these algorithms is devised to learn to
walk in a straight line, which is not sufficient to accomplish any real-world
mission. Here we introduce the Transferability-based Behavioral Repertoire
Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that
simultaneously discovers several hundreds of simple walking controllers, one
for each possible direction. By taking advantage of solutions that are usually
discarded by evolutionary processes, TBR-Evolution is substantially faster than
independently evolving each controller. Our technique relies on two methods:
(1) novelty search with local competition, which searches for both
high-performing and diverse solutions, and (2) the transferability approach,
which com-bines simulations and real tests to evolve controllers for a physical
robot. We evaluate this new technique on a hexapod robot. Results show that
with only a few dozen short experiments performed on the robot, the algorithm
learns a repertoire of con-trollers that allows the robot to reach every point
in its reachable space. Overall, TBR-Evolution opens a new kind of learning
algorithm that simultaneously optimizes all the achievable behaviors of a
robot.Comment: 33 pages; Evolutionary Computation Journal 201
Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
Automatic service composition in mobile and pervasive computing faces many
challenges due to the complex and highly dynamic nature of the environment.
Common approaches consider service composition as a decision problem whose
solution is usually addressed from optimization perspectives which are not
feasible in practice due to the intractability of the problem, limited
computational resources of smart devices, service host's mobility, and time
constraints to tailor composition plans. Thus, our main contribution is the
development of a cognitively-inspired agent-based service composition model
focused on bounded rationality rather than optimality, which allows the system
to compensate for limited resources by selectively filtering out continuous
streams of data. Our approach exhibits features such as distributedness,
modularity, emergent global functionality, and robustness, which endow it with
capabilities to perform decentralized service composition by orchestrating
manifold service providers and conflicting goals from multiple users. The
evaluation of our approach shows promising results when compared against
state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on
Artificial Intelligence and Mobile Services) on June 2
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