Natural Language Understanding and Cognitive Robotics

In the not so distant future, there is likely to be a symbiotic world of people and robots with friendly interactions between them. But for this symbiosis to occur, we need to understand the action of people (e.g., seeing, hearing, thinking, speaking, ...) and imbibe the human-like traits in robots. The most essential feature necessary for robots to achieve is that of IMU (integrative multimedia understanding) which is a natural function in humans. It allows us to assimilate pieces of information expressed through different modes such as speech, pictures, gestures, etc. The book describes the methodology to provide robots with human-like capability of natural language understanding (NLU) as the central part of IMU. The core of the methodology is mental image directed semantic theory (MIDST) which is based on the hypothesis that NLU in humans is essentially processing of mental image associated with natural language expressions, namely, mental-image based understanding (MBU). MIDST is intended to model omnisensory mental image in human and to afford a knowledge representation system in order for integrative management of knowledge subjective to cognitive mechanisms of intelligent entities such as humans and robots based on a mental image model visualized as `Loci in Attribute Spaces' and its description language Lmd (mental image description language) to be employed for predicate logic with a systematic scheme for symbol-grounding. This language works as an interlingua among various kinds of information media, and has been applied to several versions of the intelligent system IMAGES (interlingual understanding model aiming at general system). Its latest version, i.e. conversation management system (CMS) simulates human MBU and comprehends the user's intention through dialogue to find and solve problems, and finally, returns its response in text or animation. The book is aimed at researchers and students interested in artificial intelligence, robotics, or cognitive science. Based on philosophical considerations, the methodology will also have an appeal in linguistics, psychology, ontology, geography, and cartography. Key Features: Describes the methodology to provide robots with human-like capability of natural language understanding (NLU) as the central part of IMU Uses methodology that also relates to linguistics, psychology, ontology, geography, and cartography Examines current trends in machine translation