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Partial observability, or the inability of an agent to fully observe the state of its environment, exists in many real-world problem domains. However, most cognitive architectures do not have a theoretical foundation that allows for a systematic approach to handling partial observability. To address this issue, in this paper, we propose a novel cognitive architecture based on the theory of partially observable Markov decision processes (POMDPs). We focus on one of the most fundamental cognitive architectures, the blackboard architecture, and re-formulate it using the POMDP framework so that the agent can automatically decide how to use its information processing modules in partially observable environments. We propose a two-layer POMDP model for the blackboard architecture in which one layer models the dynamics of the environment and the other layer models the dynamics of the blackboard and knowledge sources within the agent. We also provide a planning method to find the optimal actions for the two-layer POMDP model based on the partially observable upper confidence bounds applied to trees (PO-UCT) algorithm. We validate the proposed architecture in both numerical and real-robot experiments.

Contribution: This article proposes a low-cost and mobile device-based learning support system for the stepper motor, which provides Web-based remote laboratory. This system allows young engineers to learn about rotational principles, control methods, and programming in stepper motors. Background: Familiarity with electrical machinery, such as motors, is an important factor for electric power engineers. Hands-on laboratories are the most effective and important learning method in the study of electrical machinery. However, the Hands-on laboratories require commitments of time, space, and significant number of expensive laboratory instruments. Intended Outcomes: In the proposed system, the students are free to repeat the experiment using a real stepper motor, for example, using a smartphone. This learning support system that combines remote laboratory and virtual laboratory is expected to be an easy-to-use for young engineers. Application Design: Proposed system consists of the low-cost microcomputers, such as raspberry Pi and Arduino, and does not use expensive software, such as LabVIEW and MATLAB. It has both low cost and compatibility with any mobile devices. Findings: The learning effectiveness and usability of the system were evaluated through questionnaires and tests for a total of 50 students at the authors’ department. Consequently, the results of the evaluation tests and questionnaires demonstrated that the proposed system will be useful to study the stepper motor.

Humanoid robots are expected to perform various jobs replacing human beings, but it is difficult for non-expert users to control them. To control humanoid robots easily, various motion imitation systems, in which a humanoid robot does the same motion as a human teacher, have been studied. In the present study, we make two contributions to building easy-to-use imitation systems. The first contribution is a flexible learning system that enables both synchronous real-time imitation, in which the robot imitates human motion almost immediately, and batch imitation, in which the robot’s motion is optimized more accurately after longer measurement of human motion. We use an efficient inference-based optimization method to enable both learning modes. The other contribution is an auditory user interface that accepts voice commands from the user and at the same time identifies the target person to be imitated. This enables the user to easily use the imitation system even when other people are present. Experimental results show that the proposed system can be used in both imitation modes and that it can identify the target person.

Recently, the optimization-by-inference approach has been proposed as a new means for solving high-dimensional optimization problems quickly. Approximate Inference COntrol (AICO) is one of the most successful and promising methods that implement the optimization-by-inference approach. AICO is able to solve stochastic optimal control problems and has already been successfully used in many applications. However, it is known that the iterative inference of AICO sometimes fails to converge to the optimal solution. To make the optimization more robust, in this paper, we propose to take model uncertainty into account. In AICO, the cost function to be minimized is accurate around a particular state of a given stochastic system, but the accuracy is uncertain in regions far from that state. Because using such an uncertain function is harmful to the convergence, we modify AICO, so that it does not use the function in uncertain regions. Our method is easy to implement and does not add much computational time to the original AICO. Experiments using two different scenarios show that our method substantially improves AICO in terms of the rate at which the algorithm produces convergent results.

One of the attractive features which a self-organizing map (SOM) possesses is a topology-preserving projection from the input layer to the competitive layer. Generally speaking, its correspondence is developed through training based on information representations of the applied multi-dimensional data. A developed feature map in the competitive layer enables us easy to understand some underlying rules visually. By the way, an analysis of Saga Prefectural sightseeing information by a SOM has been tried so far. According to the results of preceding studies, applied various topics are divided into several groups successfully. Nevertheless, there are some items not reflected in them at all. This fact implies that representations of the applied data are not appropriate to develop the feature map which we have intended in advance. Then, to overcome this tough problem, a simple idea to emphasize particular items depending on our interests is introduced as pre-synaptic processing. As a result of some computer simulations, it is confirmed that the developed feature maps are modified adaptively depending on the emphasized coefficient. And, it is concluded that the proposed simple method is effective.

The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations) can be learned at least in a class of deterministic POMDP environments

To improve the insulation performance of high voltage equipment, new functional materials of distributed permittivity, or the functionally graded materials (FGMs), have been developed. However, methods for measuring the permittivity distribution of these materials have yet to be established. In this study, numerical analyses have been conducted in order to investigate how the position of dielectrics in a cylindrical measurement device affects the capacitance and electric potential within the device. It has been confirmed that the capacitance and electric potential change with a varying position of the dielectrics. Therefore, it is suggested that the permittivity distribution of a given FGM can be estimated by measuring the capacitance and electric potential around it.

Calculating leakage reactance of transformers is a very important factor in designing transformers. It is difficult to calculate the leakage reactance in multiple-winding transformers, because their structures are more complicated. The finite element method (FEM) is useful to obtain the leakage reactance in such a complex case. Using a 3D FEM analysis, leakage reactances have been calculated and compared to experimental results. But, there were differences between the results of FEM analysis and the experimental results. In this paper, the leakage reactance is calculated by using a coupled-field analysis, which combined electromagnetic field analysis and electric circuit analysis, for simulation of short-circuit test in multiple-winding transformer. The results of this analysis showed that current values of parallelly connected primary windings are very different by the physical geometry of the secondary winding. The leakage reactances calculated by using the coupled-field analysis are qualitatively the same as experimental results.