Applications of artificial intelligence
About: Applications of artificial intelligence is a(n) research topic. Over the lifetime, 3406 publication(s) have been published within this topic receiving 64800 citation(s).
Papers published on a yearly basis
•01 Jan 1980
TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Abstract: A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, "Principles of Artificial Intelligence" describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. "Principles of Artificial Intelligence"evolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
TL;DR: A conceptual introduction to ontologies and their role in information systems and AI is provided and how ontologies clarify the domain's structure of knowledge and enable knowledge sharing is discussed.
Abstract: This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.
•01 Jan 1999
TL;DR: Rolf Pfeifer and Christian Scheier provide a systematic introduction to this new way of thinking about intelligence and computers and derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents.
Abstract: From the Publisher: Researchers now agree that intelligence always manifests itself in behavior - thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI," and "behavior-based AI.". "Rolf Pfeifer and Christian Scheier provide a systematic introduction to this new way of thinking about intelligence and computers. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building.
••04 May 2003
TL;DR: This work introduces captcha, an automated test that humans can pass, but current computer programs can't pass; any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem; and provides several novel constructions of captchas, which imply a win-win situation.
Abstract: We introduce captcha, an automated test that humans can pass, but current computer programs can't pass: any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem. We provide several novel constructions of captchas. Since captchas have many applications in practical security, our approach introduces a new class of hard problems that can be exploited for security purposes. Much like research in cryptography has had a positive impact on algorithms for factoring and discrete log, we hope that the use of hard AI problems for security purposes allows us to advance the field of Artificial Intelligence. We introduce two families of AI problems that can be used to construct captchas and we show that solutions to such problems can be used for steganographic communication. captchas based on these AI problem families, then, imply a win-win situation: either the problems remain unsolved and there is a way to differentiate humans from computers, or the problems are solved and there is a way to communicate covertly on some channels.
•01 Jan 1989
Abstract: From the Publisher: Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG. The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change. The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science. An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning. Includes new material on: Fundamentals of search, inference and knowledge representation AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems. Machine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of induction Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life. Object and agent-based problem solving and other forms of advanced knowledge representation