PBAI:Pattern Based Artificial Intelligence

In this page, we introduce PBAI (Pattern Based Artificial Intelligence).

What is the Pattern Based Artificial Intelligence?

The brain of the living things inputs and outputs pattern information. Almost all of the existing AI, however, use symbol information and is worked by the principle different from the brain of the living things. Using the technique of SOINN, developmentally, we aim to create the AI based on pattern information similar to the brain of the living things, which can robustly work in real world. For instance, the robots having PBAI can online develop intelligence, acquire a language, and solve the task by "experiments", even if it is not previously programmed, with intaraction of human beings and environment around the robots. 
The term of "Pattern based Artificial Intelligence" is coined term by Hasegawa. Hereafter, we think that, however, PBAI will become fairly significant at all intelligent information processing.

Published Papers

  1. SOINN-AM (Associative Memory)
    • Sudo Akihito; Sato Akihiro; Hasegawa Osamu, "Associative Memory for Online Learning in Noisy Environments Using Self-organizing Incremental Neural Network"], IEEE Transactions on Neural Networks, Vol.20, No.6, pp.964-972, (2009)
      Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.
  2. SOINN-PBR (Pattern Based Recognition)
    • Furao Shen, Akihito Sudo, Osamu Hasegawa, "An Online Incremental Learning Pattern-based Reasoning System", Neural Networks, Vol.23, No.1, pp.135-143, (2009)
      An architecture for reasoning with pattern-based if–then rules is proposed. By processing patterns as real-valued vectors and classifying similar if–then rules into clusters in long-term memory, the proposed system can store pattern-based if–then rules of propositional logic, including conjunctions, disjunctions, and negations. Moreover, it achieves some important properties for intelligent systems such as incremental learning, generalization, avoidance of duplicate results, and robustness to noise. Results of experiments demonstrate that the proposed method is effective for intelligent systems for solving various tasks autonomously in a real environment.
  3. Language Acquisition of robot using SOINN
    • Xiaoyuan He, Ryo Kojima and Osamu Hasegawa : "Developmental Word Grounding through A Growing Neural Network with A Humanoid Robot" , IEEE Trans. SMC-Part B, Vol.37, No.2, pp.451-462, (2007)
      This paper presents an unsupervised approach of integrating speech and visual information without using any prepared data. The approach enables a humanoid robot, Incremental Knowledge Robot 1 (IKR1), to learn word meanings. The approach is different from most existing approaches in that the robot learns online from audio-visual input, rather than from stationary data provided in advance. In addition, the robot is capable of learning incrementally, which is considered to be indispensable to lifelong learning. A noise-robust self-organized growing neural network is developed to represent the topological structure of unsupervised online data. We are also developing an active-learning mechanism, called "desire for knowledge", to let the robot select the object for which it possesses the least information for subsequent learning. Experimental results show that the approach raises the efficiency of the learning process. Based on audio and visual data, they construct a mental model for the robot, which forms a basis for constructing IKR1's inner world and builds a bridge connecting the learned concepts with current and past scenes.
    • Xiaoyuan He, Tomotaka Ogura, Akihiro Satou and Osamu Hasegawa, "Developmental Word Acquisition And Grammar Learning by Humanoid Robots through A Self-Organizing Incremental Neural Network", IEEE Trans. SMC-Part B, Vol.37, No.5, pp.1357-1372, (2007)
      We present a new approach for online incremental word acquisition and grammar learning by humanoid robots. Using no data set provided in advance, the proposed system grounds language in a physical context, as mediated by its perceptual capacities. It is carried out using show-and-tell procedures, interacting with its human partner. Moreover, this procedure is open-ended for new words and multiword utterances. These facilities are supported by a self-organizing incremental neural network, which can execute online unsupervised classification and topology learning. Embodied with a mental imagery, the system also learns by both top-down and bottom-up processes, which are the syntactic structures that are contained in utterances. Thereby, it performs simple grammar learning. Under such a multimodal scheme, the robot is able to describe online a given physical context (both static and dynamic) through natural language expressions. It can also perform actions through verbal interactions with its human partner.

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