1.1 Motivation
Traditionally, Artificial Intelligence, according to the definition of Computer Science, works as helpful machines to find solutions to complex problems in a more human-like fashion [1]. This generally involves adopted characteristics from human intelligence, and it applies them as algorithms in a computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears. Those researches, for example: Neural Network, Fuzzy Approach, Genetic Algorithm, and so on, all focus on Soft Computing. Of course, XCS (Extend Classifier System) is also a hybrid approach with high performance to the accuracy and the rule evolution on the prediction application. However, up to now, the Artificial Intelligence Techniques based on Soft Computing have all involved the concept, trial and error method or stimulus-response method even the series of evolution approaches [2,3], to construct their learning models. For this aspect, if possible, this example, a Chinese idiomatic phrase-”An Illusory Snake in a Goblet”, is taken into consideration as an input-output pattern to training the learning model. The models are formed for sure. It is actually a wrong model trained by a bad experience. Besides, the parameters of those training models are exactly affected by the input dataset, especially the large difference of the training inputs and testing ones. Usually, in many researches it is chosen the high relation between the input and output datasets or given the strong assumption which is the inputs and outputs are relevant. Thus, a subjective black-box view and the tuning view are easily concluded [4].
The other sub-domain, Expert System, which’s primary goal is to make expertise
available to decision makers and technicians who need answers quickly. There is never enough expertise to go around -- certainly it is not always available at the right place in the right time. The same systems in-depth knowledge of specific subjects can assist supervisors and managers with situation assessment and long-range planning. These knowledge-based applications of artificial intelligence have enhanced productivity in business, science, engineering, and even the military. Although, the development of those expert systems is the view of anti-extreme to construct domain knowledge first but, for the reason, they are lack of the flexibility and the adaption. In fact, each new deployment of an expert system yields valuable data for what works in which context, thus fueling the AI research that provides even better applications.
Many researches, no matter Soft Computing techniques or Expert Systems try to consider into the human-like thinking way to make the simulation. But, from classic psychology, the human-mind researches are the researches to the human-behavior. Since Plato, Psychology is an unfathomable philosophy and those advanced AI researchers should concern this perfect development of Human Psychology, from simple to complex and from single factor to multiple ones. However, the traditional AI techniques are seldom focused on the high level of human-mind process and just paid attentions to the learning definition from the Empricalism Psychology. According to the development of Modern Psychology, the core of Psychology has been already transferred Empricalism-base into Information Process Theory of Human-Mind, Cognitive Psychology-base. As for the knowledge and the model construction, the teaching-base aspect has been involved as well to the learning process.
Based on the aspect, this work tries to enhance the learning process of traditional AI techniques whose cognitive scotomas of learning definition, and it develops the novel learning model, involving the concept of Cognitive Psychology, which is utilized the high accuracy-prediction XCS model as the construction basement.
1.2 Purpose
Among learning artificial intelligence techniques, no matter neural network, fuzzy approaches, or any hybrid methods, all the models are formed by trial and error learning way, the traditional definition of learning [1]. It is practicable to be implemented that those models are utilized to a close-form problem. As for the others to unclose-form problems, however, it is critical the set of their relative input and output pairs needs to be modified.
The datasets used to train or test should be all verified first as well, which is a boring work to the model designers. Besides, the relative problems of those evolution artificial intelligence techniques are also faced to my pre-statement. It is more significant to concern the proper datasets as inputs effects the model construction. By Darwin’s Evolution Theory, Natural-Selection is easily to be concluded for the all organisms. The detailed steps could be realized that each obvious verified evolution result is always caused by the right things, the key factors, and the certain environment at the critical time. It is definitely not the random result. Take human evolution for instance, judged from the biotic evolution history of the earth – from the mitochondria, the cell, the microorganism, the multi-cell organism, …, the pithecanthrope, to Human, who dare to assure Human as the primate animal, still would own respectively two hands and two feet, each five fingers, if the history of the earth reshuffles?. That explains the reason, of which the dimension to solve problems could not be too complex, is that the training samples are not always sufficient to construct the model.
Nevertheless, much Knowledge discovery, Theory verification and Theorem definition are aggregated and not disregarded. They are all continually historical accumulated. That is also the reason that the civilization is enhanced, the culture is accumulated, and knowledge is transmitted. Either the voluntary learning or the passive learning through education is the key cores in each process. Following the previous concept, moreover, the hybrid approach, XCS [5], has already been verified its prediction accuracy and its ability to dynamical
environment and it becomes the foundation of this work to construct the knowledge learning model. The above two assumptions/pre-statements are taken into consideration to develop the efficient knowledge learning model of the self-learning and the passive-learning.
The methodologies are applying XCS with the reinforcement learning ability and involving the Human education [6] characteristic of Cognitive Psychology. Furthermore, it is the purpose to develop the high efficient learning model with the high accuracy knowledge accumulation is its purpose. The major contribution of this work is the proposed architecture. Once, the more accuracy ability of AI Techniques invented could be substituted for XCS and more performance would be more efficient.
1.3 Research Problem
The research issue will be arranged to develop the efficient knowledge learning model.
First, the learning definition would be concluded from traditional AI, especially the classifier system. Second, in this work we would try to survey the psychology, thousands year of its development, as the basement to analyze the development of AI and the learning of human behavior. Moreover, this work focuses on Modern Psychology, Cognitive Psychology, to collect and induce and its learning concept to develop an enhanced model which increases the training process and the knowledge output. As for the design of the simulation, the traditional training/learning process of XCS model would be respectively compared to the proposed learning model and the education-learning proposed model.
Finally, the performance would be verified.
1.4 Organization
The rest of this dissertation is organized as follows. In Chapter 2 we review the related work on Classifier System, Cognitive Psychology, and the Relationship of Cognitive Psychology and Classifier System. In Chapter 3, the cognitive learning from the evolved
learning is distinguished and the definition of memory from Cognitive Psychology is described. In Chapter 4 it presents the dual-mode learning mechanism by education (E) learning and reinforcement-rehearsal (R-R) learning based on XCS, which contains the description of XCS, R-R XCS, and E&R-R XCS. Chapter 5 compares the experiments with the three learning model. Nevertheless, the design of finance prediction simulation would be detailed first. Conclusions and future work are made in the final Chapter 6.