A method in which multi-valued decision diagrams (MDDs) are used to acquire robot action rules is proposed. Kanoh et al. have proposed a method, which uses multi-terminal binary decision diagrams (MTBDDs), to acquire robot action rules. But the variables of MTBDDs can only take values of 0 or 1; multiple variables are needed to represent a single joint angle. This increases the number of variables and the MTBDDs that represent the action rules become complex. Here, a method that uses MDDs, in which a single variable can take on multiple values, is proposed and experimental results are shown comparing MTBDDs and MDDs through simulations to acquire robot action rules.

Non-photorealistic rendering (NPR) is a technique used in the field of computer graphics. Here, we restrict our attention to the subset of NPR developed to synthesize drawing and painting techniques. Specifically, we focus on the style of Cubist art, such as the works of Picasso. First, we examine the actual works of Picasso in order to understand and improve the qualities of Cubist style rendering. As a result, the painting composition rules are expected to be extracted from the analysis of Picasso's works. In the present paper, we propose a Cubist style rendering based on the analysis of painting compositions. The present paper describes the proposed method by which to construct a painting composition database and illustrates the rules extracted from the database. We use rough set theory to extract the rules, and using these rules; we render and illustrate a sample image in Cubist style.

The paper describes a comparison of the sensibility of recognizing emotions from human voices speaking Japanese and Korean. Our study focuses on the emotional elements included in the human voice, and our method uses Bayesian networks of prosodic features as models of Japanese's and Korean's sensibilities in recognizing emotions. The training datasets are prosodic features extracted from emotionally expressive voice data in the two languages. Our method makes the Bayesian network learn the dependence and its strength between nonverbal voice features and its emotion. We compare the sensibilities of emotion recognition from Japanese and Koreans speech by examining the cross-inference through two Bayesian networks with speech in the other language.

A central pattern generator (CPG) and passive dynamic walking (PDW) have attracted much attention in the research field of bipedal locomotion. We describe a motion control method based on dynamic joint passivization for biped robot locomotion. CPG-based motion control is effective for walking on uneven terrain. However, it has serious problems with energy loss. In contrast, PDW saves energy because a robot can walk without any active control or energy input on a downhill slope. However, PDW robot can not walk on uneven terrian, but only on a downhill slope. We think that active walking needs to be mixed with PDW for robot walking. Our motion control method is based on a mixture of the CPG and PDW, that is, the dynamic passivization of joint control. Experiments using the motion control method based on dynamic passivization of joint control successfully generated energy efficient walking and enabled superior gaits.

Recently, as the relationship between robot and human has become closer, humans demand that robots pose familiar human-like characteristics. For a robot to live and communicate with people, it requires its own personality or individuality. Changing the mood transition of robots can change the perceptions people have of their characteristics. We propose an emotion generation model that represents a robot's internal state. This model can assess the robot's individuality through mood transitions. We report experiments of emotional conversation with a robot that had this model installed. The experimental results showed that personality could be effectively expressed by changing robot's mood transitions. We also report significant results of evaluations of psychological impact.

To improve face-to-face interaction with robots, we developed a model for generating interactive facial expressions by using a simple recurrent network (SRN). Conventional models for robot facial expression use predefined expressions, so only a limited number of expressions can be presented. This means that the expression may not match the interaction and that the person may find the expressions monotonous. Both problems can be overcome by generating expressions dynamically. We tested this model by incorporating it into a robot and comparing the expressions generated with those of a conventional model. The results demonstrated that using our model increases the diversity of face-to-face interaction with robots.

A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and reuse are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the hidden Markov model. When the motion is recognized as being unfamiliar, the second part learns it using dynamical movement primitives and acquires a knowledge of the motion. When a robot recognizes the instructed motion as familiar or judges that its acquired knowledge is applicable to the motion generation, the third part imitates the instructed motion by modifying a learned motion. This paper reports some performance results: the motion imitation of several radio gymnastics motions.

We describe a method for sensitivity communication robots which infer their dialogist's emotion. The method is based on the Bayesian approach: by using a Bayesian modeling for prosodic features. In this research, we focus the elements of emotion included in dialogist's voice. Thus, as training datasets for learning Bayesian networks, we extract prosodic feature quantities from emotionally expressive voice data. Our method learns the dependence and its strength between dialogist's utterance and his emotion, by building Bayesian networks. Bayesian information criterion, one of the information theoretical model selection method, is used in the building Bayesian networks. The paper finally proposes a reasoner to infer dialogist's emotion by using a Bayesian network for prosodic features of the dialogist's voice. The paper also reports some empirical reasoning performance.

This paper presents a method that enable a domestic robot to show emotions with its facial expressions. The previous methods using built-in facial expressions were able to show only scanty face. To express faces showing various emotion, (e.g. mixed emotions and different strengths of emotions) more facial expressions are needed. We have therefore developed a system that converts emotions into robot's facial expressions automatically. They are created from emotion parameters, which represent its emotions. We show that the system can generate facial expressions reasonably

In this paper, we propose a new behavioral model based on the concept of "meme" and "qualia" for multi-agent system. We, then, implement the model, and perform multi-agent simulations of cultural transmission. In the simulations, we construct an artificial society, where agents behave for food objects collectively. An agent transmits meme, the object to eat and its preference as a culture, to other agents. As the results of the simulations, we have observed some cultural transmissions in the artificial society.

Some research has been conducted on applying coloration of painterly art to a photographic image. Most of the proposed methods use some absolute criteria, e.g., the nearest distance in CIEL*a*b* color space, converting a color to another uniquely. However, we think that basic color transfer depends on the subjective feelings and sensitivities of users themselves. We describe an algorithm where every user can produce an original color-transformed image resulting from two inputs, which are a target photograph and a reference painting. Our algorithm is based on interactive evolutionary computation (IEC) and can produce a variety of color-transformed images semi-automatically. Some color-transformed examples similar to reference paintings are illustrated, and the searching efficiency of our method is discussed.

Real-coded genetic algorithms (GAs) are effective methods for function optimization. Generally speaking, the major crossover methods used in real-coded GAs require a large execution time for calculating the fitness of many children at each crossover. Thus, a new crossover method is needed for searching such a large search space efficiently. A novel crossover method that generates children stepwise is proposed and applied to the conventional generation-alternation model. In experiments based on standard test functions and actual problems, the proposed model found an optimal solution 30-50% faster than did the conventional model.

In authors programming course, students create animations as programming practice because this type of practice will make students interested in learning programming. This type of animation creation practice is well received by students. For the animation creation, we provide students with material such as fundamentals for creating basic animation, explanations of graphics functions, basic animation creation method, its application and its programming example in printed form (black and white). However, it is difficult for students to see the actual color or motion in printed material, because some of the animation is only shown on the screen and it is difficult for the students to actually reproduce the motion or examine the source codes by themselves. In order to overcome those problems, we have developed teaching material on the Web and actually used them in programming course. As a result it is clear that this material contributes to the increase in LOC, FP and the volume of animation created. In this paper, we give the general description of programming course, the objective of developing teaching material on the Web and guidelines for it, a general description of material, its effectiveness and problems we may see in the future.

A novel robot, Ifbot, which can communicate with humans by joyful conversation and emotional facial expression has been developed by our industry-university joint research project. This work introduces Ifbot and its mechanics and software architecture for the human-robot communication. As the fundamental technology of Ifbot for the robot-human interaction, we also propose an image processing method for real-time face tracking and talker distinction.

Many existing methods of reinforcement learning have treated tasks in a discrete low dimensional state space. However, the smooth control of humanoid robots requires a continuous high-dimensional state space. In this paper, to treat the state space, we proposed an adaptive allocation method of basis functions for reinforcement learning. Grid or incremental allocation methods have previously been proposed for allocation of basis functions. However, these methods may result in the curse of dimensionality, and a fall into local minima. On the other hand, our method avoids local minima, which are assessed by the trace of activity of basis functions. That is, if the current state is determined to have fallen into a local minimum, our method eliminates a basis function, which most affects the state. Moreover our method learns with a low number of basis functions because of the elimination process. In order to confirm the effectiveness of our method, by using computer simulation, a humanoid robot learned the motion of standing up from a chair. This motion was enabled with a small number of basis functions.

The "Ifbot" robot communicates with people by considering its own "emotions" and theirs. Ifbot has many facial expressions to communicate enjoyment. We first attempted to extract characteristics of Ifbot's facial expressions by mapping these to its emotional space, which we discuss in this paper. We applied a five-layer perceptron to the extraction. We also propose a method of seamlessly changing facial expressions using the emotional space. We report on some results of facial changes obtained with the proposed method.

In this paper, three refinements on I-SATCHMO are presented. I-SATCHMO is an application of the backjumping strategy for SATCHMO, which is a model generation theorem prover and is formalized as positive unit hyperresolution tableaux, to pruning away redundant branches. However, I-SATCHMO does not completely implement backjumping. Since marking an atom as useful whenever it contributes to a forward chaining reasoning, I-SATCHMO suffers from two problems. One is that the atoms that only contribute to irrelevant forward chaining reasoning are marked as ‘useful’. The other is that the atoms that are only useful for the sibling branches of the branch under consideration are taken as ‘useful’ for the branch. In both cases, I-SATCHMO might take some irrelevant violated clauses as ‘relevant’ and therefore use them to expand the search space. Moreover, not utilizing the partial reasoning results derivable during reasoning, I-SATCHMO might make repeated reasoning. Addressing these problems, our extension, called R-SATCHMO, refines I-SATCHMO in three ways: (1) only making the atoms that contribute to relevant forward chaining reasoning as useful; (2) it distinguishes between the set of useful atoms in different branches of a proof tree; (3) it summarizes the derived refutations as nogoods and utilizes them to avoid repeated reasoning. Hence, R-SATCHMO always searches a subspace of that which I-SATCHMO does. R-SATCHMO is optimal in the sense that during reasoning no same complete subtree is constructed in a proof tree. Moreover, R-SATCHMO can be used to generate minimal unsatisfiable subsets of an unsatisfiable set of clauses, and the strategies proposed in R-SATCHMO are applicable to minimal model generation procedures. We describe our refinements, present our implementation, prove the correctness, provide examples to demonstrate the power of our refinements, and discuss the application to minimal model generation.

Genetic algorithm (GA) is an effective method of solving combinatorial optimization problems. Generally speaking most of search algorithms require a large execution time in order to calculate some evaluation value, especially in real-world applications as well. Crossover is very important in GA because discovering a good solution efficiently requires that the good characteristics of the parent individuals be recombined. The multiple crossover per couple (MCPC) is a method that permits a number of children for each mating pair, and MCPC generates a huge amount of execution time to find a good solution. This paper proposes a novel approach to reduce time needed for fitness evaluation by "prenatal diagnosis" using fitness prediction. In the experiments based on actual problems, the proposed method found an optimum solution about 50% faster than the conventional method did. The experimental results from standard test functions show that the proposed method is applicable to other problem as well.

Thinning is a fundamental concept of computer processing of line images, and it plays an important role in pattern recognition. Most existing thinning algorithms have the problem to produce distorted skeletons. Some methods to correct the distorted skeleton have been proposed, but non of them produce complete results. In order to solve the problem, we propose an improved thinning algorithm which is a skeleton revision algorithm using maximal circles (SRAMCs). SRAMCs is a hybrid algorithm and the processing route is divided into three stages: 1) thinning using Hilditch algorithm; 2) removal of spurious pixels using maximal circle; and 3) skeleton reconstruction. The results of experiments indicates that SRAMCs can be useful for detection of lines and curves in digitized line images.

Implements a display system for cat's cradle game animation in 3D. The input to our system, finger actions, originates from a user. Based on this input, we animate the finger actions and display an operated pattern of string figures. A stabilizing procedure is in charge of obtaining a stable state of the string figures. Processing string figures on a computer is difficult, since the string's dynamics are still unknown. Our system uses a set-of-crossings representation method for processing string figures. We employ some local transformations of the string figures to stabilize them. Furthermore, topological characteristics of the string figures can be observed.

We characterize the problem of detecting edges in images as a fuzzy reasoning problem. The edge detection problem is divided into three stages: filtering, detection, and tracing. Images are filtered by applying fuzzy reasoning based on local pixel characteristics to control the degree of Gaussian smoothing. Filtered images are then subjected to a simple edge detection algorithm which evaluates the edge fuzzy membership value for each pixel, based on local image characteristics. Finally, pixels having high edge membership are traced and assembled into structures, again using fuzzy reasoning to guide the tracing process. The filtering, detection, and tracing algorithms are tested on several test images. Comparison is made with a standard edge detection technique.

A description is given of parallel control techniques and performance evaluations by simulation for the knowledge-base machine (KBM) using the multiport page-memory (MPPM) and the unification engine (UE). Relational knowledge base retrieval requires repeated application of the unification-join (U-join) operations on term relations. It also requires each U-join to be dynamically scheduled, observing the result of the previously executed U-join. Control techniques to execute the query process of the relational knowledge base effectively in the KBM using the MPPM and the UE have been investigated. For parallel execution of coarse-grain operations, each operation is decomposed to concurrently executable fine-grain operations by partitioning input data sets. A number of features that depend on processor-allocation strategies have been identified. In particular, it was found that query processing resulting in repetitions of the retrieval operation, by a certain control strategy, increases the amount of the total data transfer to the highly parallel UEs and lowers processing efficiency.>

We assume a back-end type relational data base machine equipped with multiple dedicated engines for relational database operations. Response characteristics are evaluated, and some parallel control techniques are considered for improved response time by simulating the database machine in executing relational database operations using these engines in parallel.

This paper presents a methodology for constructing a deductive database system consisting of an intensional processor and a relational database management system. A setting evaluation approach is introduced. The intensional processor derives a setting from the in-tensional database and a given goal and sends the setting and the relationship between setting elements to the management system. The management system performs a unit resolution with setting using relational operations for the extensional databases. An extended least fixed point operation is introduced to terminate all types of recursive queries.

A relational knowledge base model and an architecture which manipulates the model are presented. An item stored in the relational knowledge base is called a term. A unification operation on terms in the relational knowledge base is used as the retrieval mechanism. The relational knowledge base architecture we propose consists of a number of unification engines, several disk systems, a control processor, and a multiport page-memory. The system has a knowledge compiler to support a variety of knowledge representations.