Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Distributed classifier migration in XCS for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

学習戦略に基づく学習分類子システムの設計

On Learning Classifier Systems dubbed LCSs a leaning strategy which defines how LCSs cover a state-action space in a problem can be one of the most fundamental options in designing LCSs. There lacks an intensive study of the learning strategy to understand whether and how the learning strategy affects the performance of LCSs. This lack has resulted in the current design methodology of LCS which does not carefully consider the types of learning strategy. The thesis clarifies a need of a design methodology of LCS based on the learning strategy. That is, the thesis shows the learning strategy can be an option that determines the potential performance of LCSs and then claims that LCSs should be designed on the basis of the learning strategy in order to improve the performance of LCSs. First, the thesis empirically claims that the current design methodology of LCS, without the consideration of learning strategy, can be limited to design a proper LCS to solve a problem. This supports the need of design methodology based on the learning strategy. Next, the thesis presents an example of how LCS can be designed on the basis of the learning strategy. The thesis empirically show an adequate learning strategy improving the performance of LCS can be decided depending on a type of problem difficulties such as missing attributes. Then, the thesis draws an inclusive guideline that explains which learning strategy should be used to address which types of problem difficulties. Finally, the thesis further shows, on an application of LCS for a human daily activity recognition problem, the adequate learning strategy according to the guideline effectively improves the performance of the application. The thesis concludes that the learning strategy is the option of the LCS design which determines the potential performance of LCSs. Thus, before designing any type of LCSs including their applications, the learning strategy should be adequately selected at first, because their performance degrades when they employ an inadequate learning strategy to a problem they want to solve. In other words, LCSs should be designed on the basis of the adequate learning strategy.電気通信大学201

An XCS-Based Intelligent Searching Model for Cross-Organization Identity Management in Web Service

Internet services in distributive organization are normally built on an open network environment. In the environment internet service provisioning cannot be expected executing in a single close organization [1]. Identity management in cross-organization becomes an issue for handling Internet service and distributive business process. The “identity” in cross-organization web service is defined as global identity rather than private identity from client. Global identity searching table that registers all related service organization is the normal way we used to [2]. Through global identity searching table the target service organization can be looked up directly. For some business program, however, global identity is not necessary registered in specific service organization, e.g. IMSI (International Mobile Subscriber Identification) registration in NP (Number Portability) Service [3], for instance. In NP Service each IMSI can be re-assigned to different mobile service provider if the IMSI apply the re-assignment. In the example about IMSI in NP service mentioned above, if there is an internet service will be executed according to identity management with IMSI, then it will be a challenge to find the organization for available IMSI in very short response time. To dynamically re-assign a IMSI in different mobile service provider, the traditional global identity searching table will not be practical due to frequently changing the registration of IMSI in different service provider. To give an intelligent searching model for cross-organization global identity management is a better way than a static global identity searching table management in Web service. In this article the XCS (eXtended Classifier System) classifier system [4] will be proposed as the kernel system. With the characteristics in machine learning and rules management, the XCS-based intelligent searching model can help to predict where the web service can find the global identity in the open and cross-organization environment

Hybridizing and applying computational intelligence techniques

As computers are increasingly relied upon to perform tasks of increasing complexity affecting many aspects of society, it is imperative that the underlying computational methods performing the tasks have high performance in terms of effectiveness and scalability. A common solution employed to perform such complex tasks are computational intelligence (CI) techniques. CI techniques use approaches influenced by nature to solve problems in which traditional modeling approaches fail due to impracticality, intractability, or mathematical ill-posedness. While CI techniques can perform considerably better than traditional modeling approaches when solving complex problems, the scalability performance of a given CI technique alone is not always optimal. Hybridization is a popular process by which a better performing CI technique is created from the combination of multiple existing techniques in a logical manner. In the first paper in this thesis, a novel hybridization of two CI techniques, accuracy-based learning classifier systems (XCS) and cluster analysis, is presented that improves upon the efficiency and, in some cases, the effectiveness of XCS. A number of tasks in software engineering are performed manually, such as defining expected output in model transformation testing. Especially since the number and size of projects that rely on tasks that must be performed manually, it is critical that automated approaches are employed to reduce or eliminate manual effort from these tasks in order to scale efficiently. The second paper in this thesis details a novel application of a CI technique, multi-objective simulated annealing, to the task of test case model generation to reduce the resulting effort required to manually update expected transformation output --Abstract, page iv