ATNoSFERES revisited

ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results

PASS: a simple classifier system for data analysis

Let x be a vector of predictors and y a scalar response associated with it. Consider the regression problem of inferring the relantionship between predictors and response on the basis of a sample of observed pairs (x,y). This is a familiar problem for which a variety of methods are available. This paper describes a new method based on the classifier system approach to problem solving. Classifier systems provide a rich framework for learning and induction, and they have been suc:cessfully applied in the artificial intelligence literature for some time. The present method emiches the simplest classifier system architecture with some new heuristic and explores its potential in a purely inferential context. A prototype called PASS (Predictive Adaptative Sequential System) has been built to test these ideas empirically. Preliminary Monte Carlo experiments indicate that PASS is able to discover the structure imposed on the data in a wide array of cases

An investigation of messy genetic algorithms

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented

Symbiogenesis in learning classifier systems

Abstract Symbiosis is the phenomenon in which organisms of different species live together in close association, resulting in a raised level of fitness for one or more of the organisms. Symbiogenesis is the name given to the process by which symbiotic partners combine and unify, that is, become genetically linked, giving rise to new morphologies and physiologies evolutionarily more advanced than their constituents. The importance of this process in the evolution of complexity is now well established. Learning classifier systems are a machine learning technique that uses both evolutionary computing techniques and reinforcement learning to develop a population of cooperative rules to solve a given task. In this article we examine the use of symbiogenesis within the classifier system rule base to improve their performance. Results show that incorporating simple rule linkage does not give any benefits. The concept of (temporal) encapsulation is then added to the symbiotic rules and shown to improve performance in ambiguous/non-Markov environments

A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

自己学習型ロボットナピゲータの開発 : クラシファイアーシステムによるアフローチ

In this paper, a new approach of how to develop the Self Learning Robot Navigator isdescribed. For arbitrary navigation tasks,th is navigator searches theo ptimal solutions byapplying the Classifier System. The classifier system is a machine learning system thatlearns syntactically simple string rules (called classifiers) to guide its performance in anarbitrary environment. Based on the proposed method, a robot navigator system is constructedand some numerical experiments are carried out. The results of these experimentsshow a usefulness for the proposed method

機械学習によるAIプランナーの開発

In this paper, a new approach of solving the Process Planning Problem is described.When we consider a realization of a fully automated and flexible manufacturing system orfactory, we have to solve several difficult problems. It is well known that how to get thesolution of the Process Planning is said to be the one of the most difficult problem. Ingeneral, process planning for a designed machine part involves generating a set of plansthat outline operations, machine tools, fixtures, and tools required to produce that part as amachine component. Based on the design specifications provided by the design engineer,process planner has to determine the process plan for the designed part under considerationof minimizing a production cost, and at the same time maximizing rate of production andquality of a part. To get the good process plan requires the service of an expert processplanner who has a skillful knowledge about machining processes, machine capabilities, andso on. However this kind of expert is going to disappear. So in the last decade, there hasbeen a trend to develop an automated process planning system, since it is expected to playar ole of just like an expert process planner. As ar esult many attempts have been made,e.g.,f rom ap ractical variant approach, to at heoretical variant approach and Expert Systembased approaches. However, all of these approaches have required the conformation of predeterminedrules or procedures these are subjected to the given problem itself. Therefore,when the design specifications are changed, a new replanning is necessary. This factmeans when replanning happens, we have to determine the needed data with respect to thechanged situation. This looks so tedious. Then, in this study we propose a newly processplanning idea which will have the function of learning and autonomy based on GeneticAlgorithms