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Table of contents

Dennis Hart Mahan. Elementary Sloyd and Whittling. Elementary Reinforced Concrete Building Design.. Leonard Church Urquhart. An Introduction to Tides.

Towards physical principles of biological evolution

Nanomaterials Synthesis Design, Fabrication and Applications. Grohens Thomas Kalarikkal Beeran. Yelin Dimauro Perrin. Florian Willomitzer. Research Developments in Saline Agriculture. Thomas Commerford Martin Nikola Tesla. Peter H.

Kundrecensioner

Rhys Evans. Practical Deer Management. Charles Smith-Jones. Killing It An Education. Environmental Rights The Development of Standards. Ecotoxicology of Nanoparticles in Aquatic Systems. Charles Rangeley-Wilson. Evanthia A. Nanaki George Xydis. Conservation and Utilization of Horticultural Genetic Resources. Fundamentals of Convective Heat Transfer.

1. Overview

Rama K. Shen Liao Wang. The Princeton Review. Hidaia Mahmood Alassouli. Biomedical Signal Analysis for Connected Healthcare. Principles of Heart Valve Engineering. Emerging Voices in Natural Hazards Research. Food Applications of Nanotechnology Volume Advances in Genetics Volume Photonic Crystal Metasurface Optoelectronics Volume Prade, The mean value of a fuzzy number, Fuzzy Sets and Systems, 24, , Prade, When upper probabilities are possibility measures, Fuzzy Sets and Systems, 49, , Agarwal and H.

Najal, Possibility theory vs possibility theory in fuzzy measure theory, P. Jenkins et all, Towards context aware data fusion: Modeling and integration of situationally qualified human observations to manage uncertainty in a hard-soft fusion process, Information Fusion, 21, , Kaur, M. Bala, M.

Onuwa, Fuzzy expert system for malaria diagnosis, Oriental J. Babashamsi, A. Golzadfar, N. Yusoff, H. Ceylan, N. Conference on Artificial Intelligence, Edited by H. Prade, John Wiley and Sons, Dadios, In Tech, March, Yang, M. Liu, On possibility analysis of fuzzy data, Elsevier, Fuzzy sets and systems, 94, , Narukawa, V. Torra, T. Gakuen, Fuzzy measure and probability distributions: Distorted probabilities, Willey and Sons, Certainty Factors theory is a popular alternative to Bayesian reasoning.

Certainty Factors theory provides a judgemental approach to uncertainty management in expert systems. An expert is required to provide a certainty factor to represent the level of belief in hypothesis given that evidence has been observed.

1 Introduction

Certainty factors are used if the probabilities are not known or cannot be easily obtained. Certainty theory can manage incrementally acquired evidence, the conjunction and disjunction of hypothesis, as well as evidences with different degrees of belief.

Both Bayesian reasoning and certainty theory share a common problem: finding an expert able to quantify subjective and qualitative information. Fuzzy logic is logic that describes fuzziness. Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership rather than on the crisp membership of classical binary logic.

Unlike two-valued Boolean logic, fuzzy logic is multi-valued. To represent a fuzzy set in a computer, we express it as a function and then map the elements of the set to their degree of membership. Typical membership functions used in fuzzy expert systems are triangles and trapezoids. A linguistic variable is used to describe a term or concept with vague or fuzzy values. These values are represented in fuzzy sets. Hedges are fuzzy set qualifiers used to modify the shape of fuzzy sets.

They include adverbs such as very , somewhat , quite , more or less , and slightly. Hedges perform mathematical operations of concentration by reducing the degree of membership of fuzzy elements e. Building a fuzzy expert system is an iterative process that involves defining fuzzy sets and fuzzy rules, evaluating and then tuning the system to meet the specified requirements.

Artificial Intelligence - A Guide to Intelligent Systems [Review]

Tuning is the most laborious and tedious part in building a fuzzy system. It often involves adjusting existing fuzzy sets and fuzzy rules. Frames are used to represent knowledge in a frame-based expert system.

Systems Innovation Overview

A frame contains knowledge of a given object, including its name and a set of attributes also called slots. Frame-based systems support class inheritance, that is the process by which all characteristics of a class-frame are assumed by the instance-frame.


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The fundamental idea of inheritance is that attributes of the class-frame represent things that are typically true for all objects in the class, but are filled with data that is unique for that instance. Although frames provide a powerful tool for combining declarative and procedural knowledge, they leave the knowledge engineer with difficult decisions about the hierarchical structure of the system and its inheritance paths. Machine learning involves adaptive mechanisms that enable computers to learn from experience, learn from example, and learn by analogy.

Learning capabilities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of a number of very simple and highly interconnected processors, called neurons, which are analogous to the biological neurons in the brain. The neurons are connected by weighted links that pass signals from one neuron to another.

Each link has a numerical weight associated with it. Weights are the basic means of long-term memory in Artificial Neural Networks. They express the strength, or importance, or each neuron input. The evolutionary approach to artificial intelligence is based on the computation models of natural selection and genetics known as evolutionary computation.


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  8. Evolutionary computation combines genetic algorithms, evolution strategies and genetic programming.