SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed

Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning

This paper proposes a generative probabilistic model integrating emergent communication and multi-agent reinforcement learning. The agents plan their actions by probabilistic inference, called control as inference, and communicate using messages that are latent variables and estimated based on the planned actions. Through these messages, each agent can send information about its actions and know information about the actions of another agent. Therefore, the agents change their actions according to the estimated messages to achieve cooperative tasks. This inference of messages can be considered as communication, and this procedure can be formulated by the Metropolis-Hasting naming game. Through experiments in the grid world environment, we show that the proposed PGM can infer meaningful messages to achieve the cooperative task

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning

Self-supervised learning (SSL) using mixed images has been studied to learn various image representations. Existing methods using mixed images learn a representation by maximizing the similarity between the representation of the mixed image and the synthesized representation of the original images. However, few methods consider the synthesis of representations from the perspective of mathematical logic. In this study, we focused on a synthesis method of representations. We proposed a new SSL with mixed images and a new representation format based on many-valued logic. This format can indicate the feature-possession degree, that is, how much of each image feature is possessed by a representation. This representation format and representation synthesis by logic operation realize that the synthesized representation preserves the remarkable characteristics of the original representations. Our method performed competitively with previous representation synthesis methods for image classification tasks. We also examined the relationship between the feature-possession degree and the number of classes of images in the multilabel image classification dataset to verify that the intended learning was achieved. In addition, we discussed image retrieval, which is an application of our proposed representation format using many-valued logic.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Epidemiological survey of β-hemolytic streptococci isolated from acute pharyngitis in a private pediatric practice

金沢大学大学院医学系研究科病態検査

Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence, is one of the goals in artificial intelligence and developmental robotics. Furthermore, a computational model that enables an artificial cognitive system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes the development of a cognitive architecture using probabilistic generative models (PGMs) to fully mirror the human cognitive system. The integrative model is called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In this paper, the process of building the WB-PGM and learning from the human brain to build cognitive architectures is described.Comment: 55 pages, 8 figures, submitted to Neural Network

Effect of Ethanol in Paclitaxel Injections on the Ethanol Concentration in Exhaled Breath

BACKGROUND: Ethanol is included in certain injectable preparations of anticancer drugs to increase their solubility. Since the volume of ethanol in these preparations is approximately half of the total injection volume, the potential inhibitory effects of ethanol on the central nervous system cannot be disregarded, especially considering that patients may drive immediately after administration of the medication. Therefore, the concentration of ethanol was examined in exhaled breath after administration of paclitaxel, an anticancer medication containing ethanol. METHODS: The ethanol concentration in exhaled breath immediately after an intravenous infusion of paclitaxel was measured in 30 patients, using a balloon-type gas detector tube. Correlations between the concentration of ethanol in exhaled breath and the total amount of ethanol administered or the intravenous infusion speed were calculated. RESULTS: The mean ethanol concentration in exhaled breath was 0.028 ± 0.015 mg/L. The correlation between the ethanol concentration in exhaled breath and the total dose of ethanol was weak (R(2) = 0.25; p = 0.055), while the intravenous infusion speed showed a stronger positive correlation with the concentration of ethanol in the breath (R(2) = 0.49; p = 0.11). The maximum concentration of ethanol measured in exhaled breath (0.06 mg/L) was equivalent to 40% of the threshold for drunk driving, as specified in the Road Traffic Act in Japan. CONCLUSION: In this study, no patient had a breath ethanol concentration exceeding the legal threshold for drunk driving. However, it is still advisable for patients to avoid driving after receiving paclitaxel injections. When driving cannot be avoided, patients should wait for a sufficient time after receiving the injection before driving

Successful Treatment of Epilepsy by Resection of Periventricular Nodular Heterotopia

We report on a case of successful surgical treatment of drug-resistant epilepsy associated with a solitary lesion of periventricular nodular heterotopia (PNH). In the reported patient, intracranial ictal electroencephalography disclosed that seizures did not originate from the heterotopic nodules. However, the seizures were completely suppressed by lesionectomy of PNH alone. Epileptogenesis associated with PNH likely involves a very complex network between PNH and the surrounding cortex, and the disruption of this network may be an effective means of curing intractable, PNH-associated epilepsy

Colonization by Clostridium difficile of neonates in a hospital, and infants and children in three day-care facilities of Kanazawa, Japan

The intestinal-carriage rates of i>Clostridium difficile in neonates hospitalized in the University Hospital’s Center for Perinatal and Reproductive Health and in infants and children enrolled in two day-nurseries and a kindergarten were examined. Swab samples from the floors of these facilities were also analyzed to determine the extent of environmental contamination by this organism. C. difficile was found in the stool of only one of 40 neonates during the normal 1-week stay in the hospital after delivery. The isolate from the neonate was identical to that of her mother, as determined by PCR ribotyping, pulsed-field gel electrophoresis analysis, and toxin gene type, suggesting that the C. difficile-positive neonate acquired the organism from her mother rather than from the environment. By contrast, 47 (48.0%) of the 98 infants and children, comprising 50 enrolled in two daynurseries who were ≤3 years old and 48 enrolled in a kindergarten who were 2–5 years old, carried C. difficile. The carriage rate in infants under 2 years of age was much higher (84.4%) than in children 2 years old and older (30.3%). When analyzed according to age group, the carriage rates were 100, 75.0, 45.5, 24.0, 38.5, and 23.5% in infants and children 0, 1, 2, 3, 4, and 5 years old, respectively. The observation that several children were colonized with the same type of C. difficile strain in each day-care facility, and that the floors of day-nursery A and kindergarten C were contaminated with C. difficile strains identical to those colonizing the intestines of children enrolled in those facilities suggests that cross-infection of C. difficile among children occurs through C. difficile-carrying children or their contaminated environments. [Int Microbiol 2005; 8(1):43-48