Learning sequences of rules using classifier systems with tags

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.The objective of this paper was to obtain an encoding structure that would allow the genetic evolution of rules in such a manner that the number of rules and relationship in a classifier system (CS) would be learnt in the evolution process. For this purpose, an area that allows the definition of rule groups has been entered into the condition and message part of the encoded rules. This area is called internal tag. This term was coined because the system has some similarities with natural processes that take place in certain animal species, where the existence of tags allows them to communicate and recognize each other. Such CS is called a tag classifier system (TCS). The TCS has been tested in the game of draughts and compared with the classical CS. The results show an improving of the CS performance

RTCS: a reactive with tags classifier system

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.Publicad

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

Knowledge acquisition including tags in a classifier system

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.One of the major problems related to classifier systems is the loss of rules. This loss is caused by the genetic algorithm being applied on the entire population of rules jointly. Obviously, the genetic operators discriminate rules by the strength value, such that evolution favours the generation of the stronger rules. When the learning system works in an environment in which it is possible to generate a complete training set, the strength of the rules of the CS will reflect the relative relationship between rules satisfactorily and, therefore, the application of the genetic algorithm will produce the desired effects. However, when the learning process presents individual cases and allows the system to learn gradually from these cases, each learning interval with a set of individual cases can lead the strength to be distributed in favour of a given type of rules that would in turn be favoured by the genetic algorithm. Basically, the idea is to divide rules into groups such that they are forced to remain in the system. This contribution is a method of learning that allows similar knowledge to be grouped. A field in which knowledge-based systems researchers have done a lot of work is concept classification and the relationships that are established between these concepts in the stage of knowledge conceptualization for later formalization. This job of classifying and searching relationships is performed in the proposed classifier systems by means of a mechanism. Tags, that allows the classification and the relationships to be discovered without the need for expert knowledge

Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.Publicad

Uniform coevolution for solving the density classification problem in cellular automata

Genetic and Evolutionary Computation Conference (GECCO 2000). Las Vegas, Nevada (USA), July 8-12 2000.Uniform Coevolution is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with the density parity problem in cellular automata, where the selected examples can biased the solutions founded. The results show a high value of generality using Uniform coevolution, compared with no Co-evolutive approaches.Publicad

Neural networks robot controller trained with evolution strategies

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.Neural networks (NN) can be used as controllers in autonomous robots. The specific features of the navigation problem in robotics make generation of good training sets for the NN difficult. An evolution strategy (ES) is introduced to learn the weights of the NN instead of the learning method of the network. The ES is used to learn high performance reactive behavior for navigation and collision avoidance. No subjective information about “how to accomplish the task” has been included in the fitness function. The learned behaviors are able to solve the problem in different environments; therefore, the learning process has the proven ability to obtain a specialized behavior. All the behaviors obtained have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on the mini-robot, Khepera, has been used to learn each behavior

Hierarchical genetic algorithms for composite laminate panels stress optimisation

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.Genetic algorithms (GAs) have demonstrated to be a powerful technique for solving optimisation problems. In this article, the problem of optimising the number of plies and their stacking sequence in the design of laminated composite panels is considered. This problem has special features that makes it different from traditional problems in which GAs have been applied, which make the problem a multiobjective optimisation one. Symmetry and equilibrium constraints have also been included in the solution. A modification of the canonical GA is needed and a new perspective for solving this problem by using GA techniques is introduced

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad