International Journal of Computational Intelligence and Applications

Although Artificial Neural Networks have been satisfactorily employed in several problems, such as clustering, pattern recognition, dynamic systems control and prediction, they still suffer from significant limitations. One of them is that the induced concept representation is not usually comprehensible to humans. Several techniques have been suggested to extract meaningful knowledge from trained networks. This paper proposes the use of symbolic learning algorithms, commonly used by the Machine Learning community, such as C4.5, C4.5rules and CN2, to extract symbolic representations from trained networks. The approach proposed is similar to that used by the Trepan algorithm, which extracts symbolic representations, expressed as decision trees, from trained networks. Experimental results are presented and discussed in order to compare the knowledge extracted from Artificial Neural Networks using the proposed approach and the Trepan approach. Results are compared regarding two aspects: fidelity and comprehensibility.

This paper presents a comparative analysis of segmentation and non-segmentation based techniques for cursive handwritten word recognition. In our segmentation based technique, every word is segmented into characters, the chain code features are extracted from segmented characters, the features are fed to neural network classifier and finally the words are constructed using a string compare algorithm. In our non-segmentation based technique, the chain code features are extracted directly from words and the words are fed to a neural network classifier to classify them into word classes. To make a fair comparison, a CEDAR benchmark database is used, and the parameters such as the number of words, thresholding, resizing, feature extraction techniques, etc. are kept same for both the techniques. The experimental results and analysis show that the non-segmentation technique achieves higher recognition rate than the segmentation based technique.

This paper describes a wargaming software kit, which includes two software tools, LG-CONSTRUCTOR and LG-STRATEGIST. Both are based on the new type of game theory called Linguistic Geometry (LG) for solving higher-dimensional Abstract Board Games (ABG). LG-CONSTRUCTOR will supply warfighters at all levels with the patterns of war games. With LG-CONSTRUCTOR, a warfighter will be able to consider and visualize the most viable patterns and quickly convert them into the LG hypergame, most adequate to the current mission. The adequacy of construction will be tested on the "fly" by playing and re-playing semi-finished hypergames. Employing this hypergame, LG-STRATEGIST will generate the best strategies for the military operation for all the contesting sides of the conflict. It will plan the entire operation, assess possible courses of actions (COA) for all sides, select counteractions, control operation by re-planning in real time, and play and re-play various "what-if" scenarios.

We introduce the notion of Characteristic Distributions which is a way of representing information about the payoffs of different behaviors in a Multi-Agent System. We discuss how they can be used to simplify and structure the analysis of strategies and prove the existence of optimal environments, given a certain behavior, and that all behaviors payoff equally, when taken over all possible environments (no free lunch theorem for strategies).

Polymerase Chain Reaction (PCR) is the most important experimental technique in DNA computing. When a concentration of DNA sequence is too small to investigate, PCR amplifies the DNA sequence by the addition of a polymerase. PCR is frequently used in DNA computing, because the calculation result is usually represented by a small concentration of DNA sequence. Therefore, PCR has a crucial influence on the calculation result. The reliability of PCR needs to be improved for DNA computing. In this paper, the reliability of PCR is defined by the reproducibility of the amplified concentration of DNA sequence. The PCR protocol is adjusted to improve the reliability. Quality Engineering efficiently supports this adjustment.

In real market, the squares of stock price change rates have high autocorrelation, and the change rates show high peak and fat tail distribution. With the aim of analyzing the mechanism of the stock price change, we construct an artificial stock market composed of multiple agents whose investment strategies are represented by tree-shaped programs. The market is optimized by using a Genetic Programming so that the change of its stock price resembles that of "real" stock market statistically. In order to perform an efficient optimization and to analyze agents' behavior easily, we use ADG; Automatically Defined Groups previously proposed by authors. We show experimentally that complex changes such as real market appear in the proposed artificial market. Moreover we analyze the interaction of agents which causes realistic stock price changes.

Concentration-controlled DNA computing is presented for accomplishing a local search for the solution of a shortest path problem. In this method, the concentrations of DNA representing edges are determined according to the costs on edges, and then the hybridization process is performed. Since the concentrations of hopeless candidate solutions tend to be small after the hybridization process, a local search by concentration-controlled DNA computing is a promising approach. In order to discuss about the relationship between given costs on edges in the graph and concentrations of generated DNA paths, a simulation model of the hybridization process is used and the results of a laboratory experiment are shown.

In observing behavior of multiple autonomous robots, a microscopic observation expressed by dynamic equations is usually used. However, it is very difficult to estimate behavior of robots or mutual interactions among them in real time. Furthermore, it is hard to realize the observed system by taking consideration in all the factors of the system. On the other hand, a macroscopic observation defined by state equations is efficient for recognizing behavior of multiple robots. In this study, a quantitative observation approach is proposed on behavior of multiple robots. This approach introduces macroscopic state quantities in thermodynamics into expression of the multiple robots system. The advantage of this approach is that observation on the behavior of autonomous robots in real world can be mapped to characteristic quantities in another conceptual state space. At first, the state quantities of multiple robots system are defined and their physical meaning is discussed regarding it as quantitative observation of multiple robots. The macroscopic state quantities in thermodynamics, such as temperature, pressure and entropy, are introduced into mobile robots system. Each mobile robot is regarded as a particle in term of thermodynamic systems. Experiments show that the states of robots system can be classified from a viewpoint of the macroscopic state quantities in thermodynamics. This verifies that the macroscopic quantitative observation is efficient and applicable to controlling multiple robots system.