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Natural Language Understanding and Cognitive Robotics de Masao Yokota

Natural Language Understanding and Cognitive Robotics

Autor Masao Yokota
en Limba Engleză Hardback – 24 oct 2019

Această monografie de specialitate explorează convergența dintre lingvistica computațională și robotica cognitivă, propunând o soluție pentru una dintre cele mai mari provocări ale inteligenței artificiale: replicarea modului uman de a procesa informația multimedia. Putem afirma că lucrarea lui Masao Yokota se distinge prin introducerea teoriei MIDST (Mental Image Directed Semantic Theory), care postulează că înțelegerea limbajului natural este, în esență, un proces de gestionare a imaginilor mentale asociate expresiilor lingvistice.

Notăm cu interes modul în care autorul structurează progresia volumului, pornind de la un scenariu ipotetic — robotul casnic ideal, Anna — pentru a fundamenta necesitatea unei înțelegeri multimedia integrative (IMU). Cartea analizează riguros sistemele de traducere automată și mecanismele de analiză sintactică, culminând cu prezentarea limbajului Lmd, un interlingua capabil să unifice diverse medii de informare. Această abordare completează perspectiva oferită de Integration of Natural Language and Vision Processing de Paul Mc Kevitt, adăugând o dimensiune semantică profundă prin modelul 'Loci in Attribute Spaces', care permite robotului să ancoreze simbolurile în realitatea fizică.

În contextul operei sale, Masao Yokota face o tranziție fascinantă de la studiile sale multidisciplinare despre Japanese Animation către psihologia cognitivă aplicată în IA. Dacă lucrările anterioare explorau estetica și structura narațiunii animate, volumul de față utilizează aceleași concepte de reprezentare vizuală pentru a construi un sistem de management al conversației (CMS) capabil să genereze răspunsuri nu doar în text, ci și prin animație. Este o lucrare tehnică, dar ancorată în considerații filosofice, esențială pentru înțelegerea direcțiilor moderne în robotica socială.

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Specificații

ISBN-13: 9780367360313
ISBN-10: 0367360314
Pagini: 210
Ilustrații: 67
Dimensiuni: 156 x 234 x 11 mm
Greutate: 0.52 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States

De ce să citești această carte

Recomandăm această carte cercetătorilor și studenților în IA care doresc să depășească modelele statistice de limbaj în favoarea unei abordări cognitive autentice. Cititorul va câștiga o înțelegere profundă a modului în care imaginile mentale pot fi formalizate matematic prin limbajul Lmd, oferind un avantaj teoretic în dezvoltarea roboților capabili de interacțiuni naturale, bazate pe context și intenție, nu doar pe procesarea cuvintelor.

Despre autor

Masao Yokota este profesor de psihologie la Universitatea Nihon din Tokyo, Japonia, și a ocupat funcția de președinte al Societății Japoneze pentru Studii de Animație. Expertiza sa unică îmbină psihologia cognitivă cu analiza media și robotica. Această dualitate profesională i-a permis să dezvolte teorii inovatoare despre modul în care mintea umană procesează imaginile și limbajul, aplicând aceste principii în dezvoltarea sistemelor inteligente. Activitatea sa academică este recunoscută pentru abordarea multidisciplinară, legând arta animației de rigoarea științelor computaționale.

Descriere scurtă

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

Notă biografică

Masao Yokota received a Bachelor degree from Kyushu Institute of Technology, and Master and Doctor degrees from Kyushu University. He is now a professor of informatics at Fukuoka Institute of Technology. His research focus is on AI, especially in integrative multimedia understanding by robots in being as 'natural' as humans where natural language understanding (NLU) plays the central role. He has proposed 'Mental Image Directed Semantic Theory (MIDST)' based on a hypothesis that NLU in humans is mental image processing. MIDST provides an omnisensory mental image model and a formal language called 'Lmd (Language for Mental Image Description)'. This formal language has been already implemented on several versions of the intelligent system IMAGES including integrative multimedia understanding system IMAGES-M and conversation management system CMS. Dr. Yokota has authored numerous articles on AI and was the leader of many projects funded by Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT), and Fukuoka Institute of Technology (FIT).

Cuprins

Introduction, Anna - an ideal home robot, Intuitive human-robot interaction, Integrative multimedia understanding and natural language understanding, Knowledge and cognition, Natural Language Processing Viewed from Semantics, Trends in machine translation, Case study of current MT systems (as of October, 2018), Fundamentals for Robotic NLU, NLU in accordance with semiotics, Syntactic analysis, Semantic analysis and pragmatic analysis, Robust NLU, Response synthesis, Syntax and semantics of discourse, Cognitive Essentials for Midst, Functional model of human mind, Human knowledge and cognitive propensities, Semantics and mental image, QSIs (quasi-symbolic images) and human concept system, Primitive quasi-symbolic images, Perception of causality, Semantic articulation and QSI connectors, Negation of mental image, Imaginary space region, Computational Model of Mental Image, Atomic locus as primitive QSI, Temporal conjunctions as QSI connectors, Empty event, Attributes and Standards, Formal System, Semantic principle of Lmd, Syntax of Lmd, Tempo-logical connectives, Formulation of event concepts, Formulation of laws of the world, Fundamental Postulates and Inference Rules for Deductive System, Properties of Loci, Inference rules for deduction, Tempo-logical deduction with TLCs, Human-Specific Semantics of 4d Language as Mental Images, Conventional approaches to 4D language understanding, 4D language semantics as mental images, Formulation of concepts of spatial prepositions, Properties of static 4D concepts as human intuitive mental images, Reversal operation on spatial change event concepts as mental images, Problem Finding and Solving in Formal System, Definition of problem and task, Creation problem finding and solving, Maintenance problem finding and solving, Human Language Understanding by Robots, Two-staged robotic NLU, Robotic concept system for iHRI, Compound concept system for robots, Robot manipulation as cross-media operation via Lmd, Aware computing in robots, Homogeneous/Inhomogeneous Communication, 4d Language Understanding for Cognitive Robotics, Requirements for robotic NLU, Logical Adequacy of Lmd, Translation between NL and Lmd, Reasoning in Lmd, Anchoring via Lmd, Behavioralization via Lmd, Systematic interpretation of Lmd, Multilingual Operation Via Lmd, Meaning definition, Optimization of grammatical description for word meaning definition, Language operation via Lmd, Question answering through Lmd, Computational Model of Japanese for NLU, Brief description of basic Japanese, Phrase structure grammar for Japanese, Dependency grammar for Japanese, Sentence and discourse of Japanese, Sentence types of Japanese and phrasing, Implementation of Mental-Image Based Understanding, Configuration of CMS, MBU versus conventional NLU, Stimulus sentences to CMS and human subjects, Mental image based understanding by CMS, Problem finding and solving in CMS, Awareness control of CMS, Conclusions, References.

Recenzii

This is an excellent resource for scientists and engineers looking to develop a deep natural language understanding system for application in robotics, currently one of the most critical areas of development for artificial intelligence technologies. The book is very well written and easy to follow for readers with a good background in artificial intelligence or natural language processing. The considerable number of examples and diagrams included make this volume particularly valuable for students. The examples illustrating machine understanding of Japanese offer an exciting bonus for readers engaged in machine translation or computational linguistics.
-- J. Brzezinski, McHenry County College, Choice, 2020 Vol. 58 No. 2