Human Brain and Artificial Intelligence

Human Brain and Artificial Intelligence
Author: An Zeng
Publisher: Springer Nature
Total Pages: 348
Release: 2019-11-09
Genre: Computers
ISBN: 9811513988

This book constitutes the refereed proceedings of the workshop held in conjunction with the 28th International Conference on Artificial Intelligence, IJCAI 2019, held in Macao, China, in August 2019: the First International Workshop on Human Brain and Artificial Intelligence, HBAI 2019. The 24 full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized according to the following topical headings: computational brain science and its applications; brain-inspired artificial intelligence and its applications.

Proceedings of International Joint Conference on Advances in Computational Intelligence

Proceedings of International Joint Conference on Advances in Computational Intelligence
Author: Mohammad Shorif Uddin
Publisher: Springer Nature
Total Pages: 551
Release: 2021-05-17
Genre: Technology & Engineering
ISBN: 9811605866

This book gathers outstanding research papers presented at the International Joint Conference on Advances in Computational Intelligence (IJCACI 2020), organized by Daffodil International University (DIU) and Jahangirnagar University (JU) in Bangladesh and South Asian University (SAU) in India. These proceedings present novel contributions in the areas of computational intelligence and offer valuable reference material for advanced research. The topics covered include collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing.

Transfer Learning

Transfer Learning
Author: Qiang Yang
Publisher: Cambridge University Press
Total Pages: 394
Release: 2020-02-13
Genre: Computers
ISBN: 1108860087

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

AI 2016: Advances in Artificial Intelligence

AI 2016: Advances in Artificial Intelligence
Author: Byeong Ho Kang
Publisher: Springer
Total Pages: 731
Release: 2016-11-25
Genre: Computers
ISBN: 3319501275

This book constitutes the refereed proceedings of the 29th Australasian Joint Conference on Artificial Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016. The 40 full papers and 18 short papers presented together with 8 invited short papers were carefully reviewed and selected from 121 submissions. The papers are organized in topical sections on agents and multiagent systems; AI applications and innovations; big data; constraint satisfaction, search and optimisation; knowledge representation and reasoning; machine learning and data mining; social intelligence; and text mining and NLP. The proceedings also contains 2 contributions of the AI 2016 doctoral consortium and 6 contributions of the SMA 2016.

Constraint Networks

Constraint Networks
Author: Christophe Lecoutre
Publisher: John Wiley & Sons
Total Pages: 461
Release: 2013-03-01
Genre: Computers
ISBN: 1118617916

A major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). With this aim in mind, this book provides an accessible synthesis of the author's research and work in this area, divided into four main topics: representation, inference, search, and learning. The results obtained and reproduced in this book have a wide applicability, regardless of the nature of the problem to be solved or the type of constraints involved, making it an extremely user-friendly resource for those involved in this field.

Computational Models of Argument

Computational Models of Argument
Author: H. Prakken
Publisher: IOS Press
Total Pages: 498
Release: 2020-09-25
Genre: Computers
ISBN: 1643681079

The investigation of computational models of argument is a rich and fascinating interdisciplinary research field with two ultimate aims: the theoretical goal of understanding argumentation as a cognitive phenomenon by modeling it in computer programs, and the practical goal of supporting the development of computer-based systems able to engage in argumentation-related activities with human users or among themselves. The biennial International Conferences on Computational Models of Argument (COMMA) provide a dedicated forum for the presentation and discussion of the latest advancements in the field, and cover both basic research and innovative applications. This book presents the proceedings of COMMA 2020. Due to the Covid-19 pandemic, COMMA 2020 was held as an online event on the originally scheduled dates of 8 -11 September 2020, organised by the University of Perugia, Italy. The book includes 28 full papers and 13 short papers selected from a total of 78 submissions, the abstracts of 3 invited talks and 13 demonstration abstracts. The interdisciplinary nature of the field is reflected, and contributions cover both theory and practice. Theoretical contributions include new formal models, the study of formal or computational properties of models, designs for implemented systems and experimental research. Practical papers include applications to medicine, law and criminal investigation, chatbots and online product reviews. The argument-mining trend from previous COMMA’s is continued, while an emerging trend this year is the use of argumentation for explainable AI. The book provided an overview of the latest work on computational models of argument, and will be of interest to all those working in the field.

Artificial Intelligence and Statistics

Artificial Intelligence and Statistics
Author: William A. Gale
Publisher: Addison Wesley Publishing Company
Total Pages: 440
Release: 1986
Genre: Computers
ISBN:

A statistical view of uncertainty in expert systems. Knowledge, decision making, and uncertainty. Conceptual clustering and its relation to numerical taxonomy. Learning rates in supervised and unsupervised intelligent systems. Pinpoint good hypotheses with heuristics. Artificial intelligence approaches in statistics. REX review. Representing statistical computations: toward a deeper understanding. Student phase 1: a report on work in progress. Representing statistical knowledge for expert data analysis systems. Environments for supporting statistical strategy. Use of psychometric tools for knowledge acquisition: a case study. The analysis phase in development of knowledge based systems. Implementation and study of statistical strategy. Patterns in statisticalstrategy. A DIY guide to statistical strategy. An alphabet for statistician's expert systems.

Uncertainty in Artificial Intelligence

Uncertainty in Artificial Intelligence
Author: Laveen N. Kanal
Publisher: North Holland
Total Pages: 509
Release: 1986
Genre: Artificial intelligence
ISBN: 9780444700582

Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

Brains, Behavior, and Robotics

Brains, Behavior, and Robotics
Author: James Sacra Albus
Publisher: BYTE
Total Pages: 376
Release: 1981
Genre: Medical
ISBN:

Mind and matter. The basic elements of the brain. Sensory input. The central nervous system. Hierarchical goal-directed behavior. A neurological model. Modeling the higher functions. Robots. Hierarchical robot-control systems. Artificial intelligence. Future applications. Economic, social, and political implications.