Artificial Intelligence: Structures and Strategies for Complex Problem Solving
01 Jan 1989-
TL;DR: In this paper, the authors combine the theoretical foundations of intelligent problem-solving with data structures and algorithms needed for its implementation, including logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG.
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
TL;DR: This work introduces the reader to the motivations for solving the ambiguity of words and provides a description of the task, and overviews supervised, unsupervised, and knowledge-based approaches.
Abstract: Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.
01 Jan 2003
TL;DR: This book discusses Bayesian Reasoning, Bayesian Network Applications, and Knowledge Engineering with Bayesian Networks I and II.
Abstract: Bayesian Reasoning. Introduction to Bayesian Networks. Inference in Bayesian Networks. Bayesian Network Applications. Bayesian Planning and Decision-Making. Bayesian Network Applications II. Learning Bayesian Networks I. Learning Bayesian Networks II. Causality vs. Probability. Knowledge Engineering with Bayesian Networks I. Knowledge Engineering with Bayesian Networks II. Application Software.
•01 Jan 2004
TL;DR: This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way, and offers the first true synthesis of the field in over a decade.
Abstract: Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs. *Authors are well-recognized experts in the field who have applied the techniques to real-world problems * Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems *Offers the first true synthesis of the field in over a decade Table of Contents 1 Introduction * 2 The Language of First-Order Logic *3 Expressing Knowledge * 4 Resolution * 5 Horn Logic * 6 Procedural Control of Reasoning * 7 Rules in Production Systems * 8 Object-Oriented Representation * 9 Structured Descriptions * 10 Inheritance * 11 Numerical Uncertainty *12 Defaults *13 Abductive Reasoning *14 Actions * 15 Planning *16 A Knowledge Representation Tradeoff * Bibliography * Index
TL;DR: The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application, mainly because of their symbolic reasoning, flexibility and explanation capabilities.
Abstract: Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more popular nowadays. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI has been used in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting and control of complex systems. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application. Problems presented include three areas: forecasting and modeling of meteorological data, sizing of photovoltaic systems and modeling, simulation and control of photovoltaic systems. Published literature presented in this paper show the potential of AI as design tool in photovoltaic systems.
TL;DR: The present review focuses on defining hybrid modeling, the advantages of such combined models, as well as the history and potential future of their application in hydrology to predict important processes of the hydrologic cycle.
Abstract: Summary Accurate and reliable water resources planning and management to ensure sustainable use of watershed resources cannot be achieved without precise and reliable models. Notwithstanding the highly stochastic nature of hydrological processes, the development of models capable of describing such complex phenomena is a growing area of research. Providing insight into the modeling of complex phenomena through a thorough overview of the literature, current research, and expanding research horizons can enhance the potential for accurate and well designed models. The last couple of decades have seen remarkable progress in the ability to develop accurate hydrologic models. Among various conceptual and black box models developed over this period, hybrid wavelet and Artificial Intelligence (AI)-based models have been amongst the most promising in simulating hydrologic processes. The present review focuses on defining hybrid modeling, the advantages of such combined models, as well as the history and potential future of their application in hydrology to predict important processes of the hydrologic cycle. Over the years, the use of wavelet–AI models in hydrology has steadily increased and attracted interest given the robustness and accuracy of the approach. This is attributable to the usefulness of wavelet transforms in multi-resolution analysis, de-noising, and edge effect detection over a signal, as well as the strong capability of AI methods in optimization and prediction of processes. Several ideas for future areas of research are also presented in this paper.