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The best-of-n problem (BSTn) is a collective decision-making problem in which a swarm of robots needs to make a collective decision about a set of n choices; specifically, to decide what choice offers the best alternative [1]. The BSTn captures the structure and logic of the discrete consensus achievement problems that appear in several swarm robotics scenarios. Although numerous algorithms have been proposed recently to deal with more than two choices, the number of choices that can be dealt with is not large. The bias and raising threshold (BRT) algorithm proposed by Phung et al. [2] enables swarms to deal with a large number of choices (n≫2). However, the algorithm’s goodness has not been evaluated in any practical problems, and it is necessary to evaluate the algorithm in a problem where a large number of choices exist. In this paper, we consider the best of proportions (BOP) problem; that is a version of BSTn in which a large number of choices can be dealt with by adjusting the values of different proportions. In previous research on swarms that needed to solve the BOP problem, there is only a study on the response threshold models for the division of labor. In the present study, we investigate a scenario of the BOP and apply the BRT algorithm to find the best proportion. In our previous work [3], a fixed proportion setting method has been adopted. Here, we adopt a stochastic proportion setting method to verify the relationship between the efficiency and the number of choices in a more general case. The results show that with a larger number of choices, the decision making becomes more efficient with high equality; that is a result that has not been found in [3].

We propose a method that uses a large number of digital photographs to produce highly accurate estimates of the locations of subjects that have attracted a crowd’s attention. Recently, a very active area of research has been to use humans as sensors in realworld observations that require a large amount of data. Some of these studies have attempted to produce real-time estimates of the subjects that are attracting a crowd’s attention by quickly collecting a large number of photographs. These studies are based on the assumption that, when photographers encounter interesting events, they take pictures. Some of the proposed methods realize high availability by using only photographing information, which includes information about location and azimuth of the camera and it is automatically embedded into photograph. Since this data is very small compared to that of the pixel information, the load on the communication infrastructure is reduced. However, there are problems with the accuracy when there are many attractive subjects in a small region, and they cannot be found with traditional methods that use a sequential search strategy. The proposed method overcomes this problem by applying nonnegative matrix factorization (NMF) to the estimated location of each subject. We verified the effectiveness of this by computational experiments and an experiment under a realistic environment.

We propose a framework for sharing photographs that reduces a load on communication network. When interesting events occur, many people take photographs of the events. Collecting photographs of such events and quickly building image database would enable us to share information in real-time more concretely than when using text based methods. However, a simple approach sending all photographs to a server immediately may cause congestion of a network infrastructure. In this paper, we propose a digital photographs classification method based on a main object of a photograph without using pixel information. Our method uses photographing information, that includes information about location and azimuth of a camera. Photographing information is automatically embedded into the photographs when it was taken. Our method determines main objects of photographs (socalled subject) and classifies the photographs based on these subjects. We can suppress congestion because only the best photographs transmitted to a server. In addition, classification accuracy is high because of the effect of collective intelligence. In this paper, we confirmed the effectiveness of our method through a series of experiments using a commercially available digital camera.

In this paper, a methodology for self-organization of autonomous agents is proposed. Agents make an organization to adapt their environment, building an adaptable soccer team. In soccer games, each player must forecast correctly, and players behave strongly as a team. They must also react and reform team plays if accidents happen by statistical movement of the ball. To adapt the soccer environment, we introduce an on-time communication style among autonomous agents, pheromone communication. Agents as players give their forecasting (represented by pheromones) acquired by the learning ability of each agent. Using by forecasting, they discuss and reform team play. With this learning ability in forecasting by each player, we expect they will make a close soccer team. When they start learning, they cannot behave sophisticatedly but, after sufficient learning trials, they behave like offensive professional players.

We propose a neuron with noise generator specially for the evolutionary robotics approach to incremental knowledge acquisition. Genetically evolving neural networks are modified continuously by genetic operations. Difficulty in incrementing knowledge when a neural network acts as a robotic controller arises when network operates unlike in the past due to disturbance by neurons added by genetic operators. To evolve a network robust against such internal noise, we propose adding noise generators to neurons. We show the effectiveness of the application of a logistic chaos noise generator to neurons by comparing several noise generators.