Hideyuki Nakashima

Bio: Hideyuki Nakashima is an academic researcher from Future University Hakodate. The author has contributed to research in topics: Ubiquitous computing & Ambient intelligence. The author has an hindex of 20, co-authored 75 publications receiving 1778 citations. Previous affiliations of Hideyuki Nakashima include Japan Advanced Institute of Science and Technology & National Institute of Advanced Industrial Science and Technology.

Handbook of Ambient Intelligence and Smart Environments

Abstract: Ambient Intelligence (AmI) has recently been adopted as a term referring to a multidisciplinary subject which embraces a variety of pre-existing fields of computer science and engineering. Given the diversity of potential applications this relationship naturally extends to other areas of science, such as education, health and social care, entertainment, sports, and transportation, to name a few. AmI brings these resources and many other areas together to provide flexible and intelligent services to users acting in their environments. Handbook of Ambient Intelligence and Smart Environments is a comprehensive presentation of the latest developments in the burgeoning research area of ambient intelligence and smart environments. Written by leading international experts, this seminal reference organizes all major concepts, theories, methodologies, trends, and challenges into a coherent, unified repository. About this handbook: Offers a current and thorough review of Ambient Intelligence and examines the relative physical infrastructure of smart environments Features application-oriented coverage and presents current projects on the subject Provides coverage from leading researchers and practitioners in computer science and engineering communities Describes infastructure and how sensors are networked and utilized in application settings Explores technology that can be built over a networked sensing infrastructure to make resources widely available in an unobtrusive way Studies the interaction between humans and artifiical systems Probes developments that aim to make artificial systems more rational Addresses Multi-Agent Systemssystems that contribute to the resources of an artificial system which can be used to understand different sitautions and decide intelligently Investigates a wide range of application ans well as consideration of the impact this technology can have in daily lives. Provides insight into some of the recent major projects developed around the world. This complete volume is an exceptional tool for research scientists, practitioners, senior undergraduate and graduate students in computer science and engineering. This book also presents a useful text for professionals working in service science, education, education, health and social care, entertainment, sports, transportation and urban development.

Handbook of Ambient Intelligence and Smart Environments

Abstract: Ambient Intelligence (AmI) has recently been adopted as a term referring to a multidisciplinary subject which embraces a variety of pre-existing fields of computer science and engineering. Given the diversity of potential applications this relationship naturally extends to other areas of science, such as education, health and social care, entertainment, sports, and transportation, to name a few. AmI brings these resources and many other areas together to provide flexible and intelligent services to users acting in their environments. Handbook of Ambient Intelligence and Smart Environments is a comprehensive presentation of the latest developments in the burgeoning research area of ambient intelligence and smart environments. Written by leading international experts, this seminal reference organizes all major concepts, theories, methodologies, trends, and challenges into a coherent, unified repository. About this handbook: Offers a current and thorough review of Ambient Intelligence and examines the relative physical infrastructure of smart environments Features application-oriented coverage and presents current projects on the subject Provides coverage from leading researchers and practitioners in computer science and engineering communities Describes infastructure and how sensors are networked and utilized in application settings Explores technology that can be built over a networked sensing infrastructure to make resources widely available in an unobtrusive way Studies the interaction between humans and artifiical systems Probes developments that aim to make artificial systems more rational Addresses Multi-Agent Systemssystems that contribute to the resources of an artificial system which can be used to understand different sitautions and decide intelligently Investigates a wide range of application ans well as consideration of the impact this technology can have in daily lives. Provides insight into some of the recent major projects developed around the world. This complete volume is an exceptional tool for research scientists, practitioners, senior undergraduate and graduate students in computer science and engineering. This book also presents a useful text for professionals working in service science, education, education, health and social care, entertainment, sports, transportation and urban development.

Ambient Intelligence and Smart Environments: A State of the Art

Abstract: Advances in the miniaturization of electronics is allowing computing devices with various capabilities and interfaces to become part of our daily life. Sensors, actuators, and processing units can now be purchased at very affordable prices. This technology can be networked and used with the coordination of highly intelligent software to understand the events and relevant context of a specific environment and to take sensible decisions in real-time or a posteriori.

Research directions for service-oriented multiagent systems

Abstract: Today's service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrow's successful service-oriented computing approaches. This article describes a 15-year roadmap for service-oriented multiagent system research.

Inferring long-term user properties based on users' location history

Abstract: Recent development of location technologies enables us to obtain the location history of users. This paper proposes a new method to infer users' longterm properties from their respective location histories. Counting the instances of sensor detection for every user, we can obtain a sensor-user matrix. After generating features from the matrix, a machine learning approach is taken to automatically classify users into different categories for each user property. Inspired by information retrieval research, the problem to infer user properties is reduced to a text categorization problem. We compare weightings of several features and also propose sensor weighting. Our algorithms are evaluated using experimental location data in an office environment.

Situations and Attitudes.

Abstract: In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

The art of Prolog

Abstract: ly, this program guesses nondeterministically the correct permutation via permutation(Xs,Ys), and ordered checks that the permutation is actually ordered. Operationally, the behavior is as follows. A query involving sort is reduced to a query involving permutation and ordered. A failure-driven loop ensues. A permutation of the list is generated by permutation and tested by ordered. If the permuted list is not ordered, the execution backtracks to the permutation goal, which generates another permutation to be tested. Eventually an ordered permutation is generated and the computation terminates. Permutation sort is a highly inefficient sorting algorithm, requiring time super-exponential in the size of the list to be sorted. Pushing the tester into the generator, however, leads to a reasonable algorithm. The generator for permutation sort, permutation, selects an arbitrary element and recursively permutes the rest of the list. The tester, ordered, verifies that the first two elements of the permutation are in order, then recursively checks the rest. If we view the combined recursive permutation and ordered goals as a recursive sorting process, we have the basis for insertion sort, Program 3.21. To sort a list, sort the tail of the list and insert the head of the list into its correct place in the order. The arbitrary selection of an element has been replaced by choosing the first element. Another example of the advantage of intertwining generating and testing can be seen with programs solving the N queens problem. The N queens problem requires the placement of N pieces on an Nby-N rectangular board so that no two pieces are on the same line: horizontal, vertical, or diagonal. The original formulation called for 8 queens to be placed on a chessboard, and the criterion of not being on the same line corresponds to two queens not attacking each other under the rules of chess. Hence the problem's name. 253 Nondeterministic Programming

Multiagent Systems: A Survey from a Machine Learning Perspective

Abstract: Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goals Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.

Internet of Things: Applications and Challenges in Technology and Standardization

Abstract: The phrase Internet of Things (IoT) heralds a vision of the future Internet where connecting physical things, from banknotes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it--leading to innovative services and increase in efficiency and productivity. This paper studies the state-of-the-art of IoT and presents the key technological drivers, potential applications, challenges and future research areas in the domain of IoT. IoT definitions from different perspective in academic and industry communities are also discussed and compared. Finally some major issues of future research in IoT are identified and discussed briefly.

Internet of Things: Applications and Challenges in Technology and Standardization

Abstract: The phrase Internet of Things (IoT) heralds a vision of the future Internet where connecting physical things, from banknotes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it leading to innovative services and increase in efficiency and productivity. This paper studies the state-of-the-art of IoT and presents the key technological drivers,potential applications, challenges and future research areas in the domain of IoT. IoT definitions from different perspective in academic and industry communities are also discussed and compared. Finally some major issues of future research in IoT are identified and discussed briefly.