In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection

Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific

intrusions involving sendmail and lpr. This work is part of a research program aimed at building computer security systems that incorporate the mechanisms and algorithms used by natural immune systems. 1 Introduction We are interested in developing computer security methods that are based on the way natural

Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent

. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. 1

satisfaction problems illuminates the relative and absolute effectiveness of these methods. A general model of partial constraint satisfaction is proposed. 1 Introduction Constraint satisfaction involves finding values for problem variables subject to constraints on acceptable combinations of values

The concept of an agent has recently become important in Artificial Intelligence (AI), and its relatively youthful subfield, Distributed AI (DAI). Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design

processor is presented. 1 Introduction Artificial reverberation has been used for dozens of years to add reverberation to studio recordings. The first digital reverberator was developed by Schroeder [1] over 30 years ago. His solution was based on a recursive structure constructed of parallel comb filters

in a distributed fashion is worthwhile when the problems solved by individual agents are loosely-coupled. 1 Introduction Cooperative distributed problem solving (CDPS) is a subfield of AI that is concerned with how a set of artificially intelligent agents can work together to solve problems. Recently