A Computational Model for the Cognitive Immune System Theory Based on Learning Classifier Systems

Voigt, Daniel; Wirth, Henry; Dilger, Werner

doi:10.1007/978-3-540-73922-7_23

Cited by 6 publications

(16 citation statements)

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“…Through this interaction it may well be possible to begin the development of new, useful and distinctive algorithms and systems, that go way beyond what engineering has to offer to date. Indeed, at the recent ICARIS conference a number of papers were dedicated to this and explore the usefulness of tunable activation thresholds [52,86], Cohen's cognitive model [27,112] and immune networks [57,78]. However, there is one word of caution in the excitement of modelling, and we echo the concerns of Neal and Trapnel [83] (Sect.…”

Section: Reflections On Artificial Immune Systemsmentioning

confidence: 78%

An interdisciplinary perspective on artificial immune systems

et al. 2008

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This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner.

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Section: Reflections On Artificial Immune Systemsmentioning

confidence: 78%

An interdisciplinary perspective on artificial immune systems

et al. 2008

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“…The genes of a receptor's binding site are composed individually by rearranging short pieces of DNA. After that, a random sequence of mutation, deletion, and addition operators is applied to the segments of the receptor's genetic “building plan” [7], and the development and survival of lymphocytes are determined by signals received through their antigen receptors [5]. …”

Section: Discussionmentioning

confidence: 99%

“…The ability to discriminate between relevant and irrelevant signals is called the specificity of recognition [7] (see Figure 3). …”

Section: Discussionmentioning

confidence: 99%

“…This molecular image is the starting point for the cellular interactions of the next phase where an appropriate ISR is selected [7]. …”

Section: Discussionmentioning

confidence: 99%

“…The cells react in accordance with their recent perceptions and as a result influence the reactions of other cells, and thus the ISR is a coherent system [7] (see Figure 4). …”

Section: Discussionmentioning

confidence: 99%

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A Cognitive Computational Model Inspired by the Immune System Response

Al-Hady

Badr

Mostafa

2014

BioMed Research International

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The immune system has a cognitive ability to differentiate between healthy and unhealthy cells. The immune system response (ISR) is stimulated by a disorder in the temporary fuzzy state that is oscillating between the healthy and unhealthy states. However, modeling the immune system is an enormous challenge; the paper introduces an extensive summary of how the immune system response functions, as an overview of a complex topic, to present the immune system as a cognitive intelligent agent. The homogeneity and perfection of the natural immune system have been always standing out as the sought-after model we attempted to imitate while building our proposed model of cognitive architecture. The paper divides the ISR into four logical phases: setting a computational architectural diagram for each phase, proceeding from functional perspectives (input, process, and output), and their consequences. The proposed architecture components are defined by matching biological operations with computational functions and hence with the framework of the paper. On the other hand, the architecture focuses on the interoperability of main theoretical immunological perspectives (classic, cognitive, and danger theory), as related to computer science terminologies. The paper presents a descriptive model of immune system, to figure out the nature of response, deemed to be intrinsic for building a hybrid computational model based on a cognitive intelligent agent perspective and inspired by the natural biology. To that end, this paper highlights the ISR phases as applied to a case study on hepatitis C virus, meanwhile illustrating our proposed architecture perspective.

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