In the not so distant future, we can expect a world where humans
and robots coexist and interact with each other. For this to occur,
we need to understand human traits, such as seeing, hearing,
thinking, speaking, etc., and institute these traits in robots. The
most essential feature necessary for robots to achieve is that of
integrative multimedia understanding (IMU) which occurs naturally
in humans. It allows us to assimilate pieces of information
expressed through different modes such as speech, pictures,
gestures, etc. The book describes how robots acquire traits like
natural language understanding (NLU) as the central part of IMU.
Mental image directed semantic theory (MIDST) is its core, and is
based on the hypothesis that NLU is essentially the 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 interlingual understanding model aiming at general system
(IMAGES). Its latest version, i.e. conversation management system
(CMS) simulates MBU and comprehends the user's intention through
dialogue to find and solve problems, and finally, provides a
response in text or animation. The book is aimed at researchers and
students interested in artificial intelligence, robotics, and
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
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