An articial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security, in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be eective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and dierences between ARTIS and Holland's classier systems are discussed. 1 INTRODUCTION The biological immune system (IS) is highly complicated and appears to be precisely tuned to the problem of detecting and eliminating infections. We believe that the IS provides a compelling example of a massively-parallel adaptive information-processing system, one which we can study for the purpose o...

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We describe an artificial immune system (AIS) that is distributed, robust, dynamic, diverse and adaptive. It captures many features of the vertebrate immune system and places them in the context of the problem of protecting a network of computers from illegal intrusions.

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This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor --- a sequence of steps --- with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think.

We will discuss in which conditions we can expect the emergence of agents able to integrate sensory-motor information over time and later use this information to modulate their behavior accordingly. In doing so we will illustrate the problems that these agents should be able to solve and the processes that might lead to a transition from simple agents that only rely on sensory information or on their internal dynamic to agents that are also able to integrate information over time. The analysis of evolved individuals revealed that: (1) individuals able to integrate information over time rely on mixed strategy in which basic sensory-motor mechanisms are complemented and enhanced with additional internal mechanisms; (2) evolved individuals tend to rely on partial, action-oriented, and action-mediated representations of the external environment. 1

In this paper I will show how reactive agents can solve relatively complex tasks without requiring any internal state and I will demonstrate that this is due to their ability to coordinate perception and action. By acting (i.e. by modifying their position with respect to the external environment and/or the external environment itself), agents partially determine the sensory patterns they receive from the environment. As I will show, agents can take advantage of this ability to: (1) select sensory patterns that are not affected by the aliasing problem and avoiding those that are; (2) select sensory patterns in which groups of patterns requiring different answers do not strongly overlap; (3) exploit the fact that, given a certain behavior, sensory states might indirectly encode information about useful features of the environment; (4) exploit emergent behaviors resulting from a sequence of sensory-motor loops and from the interaction between the robot and the environment. Final...

This paper presents ongoing work towards building an autonomous robot that learns in a social context. The mode of

this article we will briefly illustrate the method and the main idea, we will review the most significant contribution in this area and finally, we will discuss some of the contributions that this research area is making to the foundational debate in cognitive science. Introduction The basic idea behind evolutionary robotics [1, 2], i.e. the attempts to synthesize robots through evolutionary techniques [3-5], goes as follows. An initial population of different artificial chromosomes, each encoding the control system (and sometimes the morphology) of a robot, are randomly created and put in the environment. Each robot is then let free to act (move, look around, manipulate) according to a genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots are allowed to reproduce by generating copies of their genotypes with the addition of changes introduced by some genetic operators (e.g., mutations, crossover, dup

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