Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

2015 ◽  
Author(s):  
Razieh Falahian ◽  
Maryam Mehdizadeh Dastjerdi ◽  
Shahriar Gharibzadeh
Keyword(s):  
Artificial Intelligence ◽  
Complex Dynamics ◽  
Biological Systems

The Engineer

Rebel Genius ◽  
2016 ◽  
Author(s):  
Tara H. Abraham
Keyword(s):  
Artificial Intelligence ◽  
Reticular Formation ◽  
Research Laboratory ◽  
Biological Systems ◽  
Theoretical Modelling ◽  
Research Culture ◽  
Cognitive Sciences ◽  
New Research ◽  
Scientific Identity ◽  
The Brain

This chapter examines the ways that McCulloch’s new research culture at MIT’s Research Laboratory of Electronics shaped the evolution of his scientific identity into that of an engineer. This was an open, fluid, multidisciplinary culture that allowed McCulloch to shift his focus more squarely onto understanding the brain from the perspective of theoretical modelling, and to promote the cybernetic vision to diverse audiences. McCulloch’s practices, performed with a new set of student-collaborators, involved modeling the neurophysiology of perception, understanding reliability in biological systems, and pursuing knowledge of the reticular formation of the brain. The chapter provides a nuanced account of the relations between McCulloch’s work and the emerging fields of artificial intelligence and the cognitive sciences. It also highlights McCulloch’s identities as sage-collaborator and polymath, two roles that in part were the result of his students’ observations and in part products of his own self-fashioning.

NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS

2009 ◽  
Vol 18 (04) ◽  
pp. 487-516 ◽  
Author(s):  
VASSILIOS VASSILIADIS ◽  
GEORGIOS DOUNIAS
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Biological Systems ◽  
Artificial Immune Systems ◽  
Rapid Evolution ◽  
Successful Implementation ◽  
Human Immune System ◽  
Survey Paper ◽  
The Way

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.

scholarly journals The ideological discourse of quasi-communication

Obraz ◽  
2019 ◽  
Vol 3 (32) ◽  
pp. 39-52
Author(s):  
Ihor Pavlyuk
Keyword(s):  
Artificial Intelligence ◽  
Biological Systems ◽  
Scientific Methodology ◽  
Human Society ◽  
Marginal Values ◽  
European Philosophy ◽  
Historical Origin ◽  
General Scientific ◽  
English Speaking ◽  
The Individual

The purpose of this article is to analyze the archetypical models of quasi-confrontation in artistic and documentary texts through ideological matrices (lie-defense, lie-fear, like aggression, irony, hypnosis, meditation, lie-ritual) of social and biological systems, creative individuals, the concept of game and battle in human society, flora and fauna on the individual and global levels encoded in images is shown. Methods of research: phenomenological (unbiased description), comparative (comparison of quasi-confessional models of behavior in the animal-plant world and human society and text expression in different hourly coordinates), psychoanalytic (sounding archetypes), hermeneutic (penetration into the meanings of texts), semiotic, game (the disclosure of the mechanisms of the development of the phenomenon), deconstructivist (search for marginal values in texts and meanings of consciousness). Results and conclusions of the study. Since we have proved that in any society it is impossible to get rid of quasi-communication, this article – an attempt to offer methods for its recognition, how to create a general scientific methodology of recognition, the apparatus of categories, does not exist without metaphysical reductionism, philosophical dualism, the natural and historical origin of the phenomena of the psyche and consciousness, natural and artificial intelligence, the synthesis of analytical traditions of English-speaking philosophy and dialectical traditions of continental European philosophy, interrelations of natural, technical, social, cultural, humanitarian, historical disciplines. The biggest contradiction and the biggest paradox that most often turns “honest” communication into quasi-communication is that communication is individually-national in form and mass international, global in content. Keywords: quasi-communication, ideology, archetype, lie, game, battle, lie detector, text..

The Nature and Legal Features of Cyber-Physical, Cyber-Biological and Artificial Cognitive Systems

2019 ◽  
pp. 75-81
Author(s):  
Dmitry Kuteynikov ◽  
Osman Izhaev ◽  
Sergey Zenin ◽  
Valerian Lebedev
Keyword(s):  
Artificial Intelligence ◽  
Social Relations ◽  
Physical System ◽  
Biological Systems ◽  
Legal Framework ◽  
Cognitive Systems ◽  
Cyber Physical System ◽  
Ability To Act ◽  
Key Characteristics ◽  
Scientific Achievements

In the article, such categories as cyber-physical, cyber-biological and artificial cognitive systems (artificial intelligence) are analyzed in order to determine their characteristics important for legal science and practice. Different ways of defining the above mentioned concepts are examined. It is determined that a cyber-physical system includes a variety of technical means and is not easily placed within legal framework. Incorporation of this term in legal regulations through the description of its key characteristics is recommended. A cyber-biological system has the same structure as a cyber-physical system except that the physical component is replaced by biological one. It is argued that the relevance of the analysis of cyber-biological systems will depend on further scientific achievements in this area. The crucial property of the artificial cognitive system (artificial intelligence) is the ability to act independently and rationally. The authors conclude that the technical means covered by cyber-physical and cyber-biological systems acquire autonomy only if they have artificial intelligence. Finally, it is stated that the scope of social relations arising in the new reality will include only the technical means (objects regardless of their nature: physical, biological or virtual) able to perform legally significant actions independent from an individual person.

Simulation-Based Study of Biological Systems with Threshold Policy by a Differential Linear Complementarity System

2021 ◽  
Vol 31 (09) ◽  
pp. 2130025
Author(s):  
Jianfeng Luo ◽  
Yi Zhao
Keyword(s):  
Biological System ◽  
Complex Dynamics ◽  
Biological Systems ◽  
Threshold Value ◽  
Linear Complementarity ◽  
Continuous Control ◽  
Hiv Replication ◽  
Threshold Policy ◽  
Intrinsic Mechanism ◽  
Multiple Threshold

Threshold policy is more realistic than continuous control for biological system management. Most related works are devoted to studying a single-threshold value for one single population, thereby avoiding complicated mathematical analysis of the nonsmooth differential equations. Based on the fact that numerical simulations play an important role in analyzing and understanding the intrinsic mechanism of a biological experiment and system, we hereby propose a differential linear complementarity system to reformulate the biological system with threshold policy. Using this method, we can transform a biological system with multiple-threshold values for one or more population to a differential linear complementarity system, where the corresponding dynamics can be investigated numerically by various algorithms for the complementarity problem. Firstly, the well-posedness of solutions of the differential linear complementarity system and its discretized method are derived explicitly. Then we illustrate the application of our approach to two systems which are a population harvesting system with threshold policy and an HIV replication system with threshold therapy, respectively. Numerical results demonstrate that those nonsmooth biological systems exhibit much more complex dynamics than the corresponding smooth systems. These results also validate the effectiveness and simplicity of the method that reformulates a common biological system with multiple-threshold policy by a differential linear complementarity system.

scholarly journals Imperfect Automata: Effects of “mutation” on the evolution of automaton 01101110 (Rule 110)

2019 ◽  
Author(s):  
Andrei Popa
Keyword(s):  
Artificial Intelligence ◽  
Initial Conditions ◽  
Biological Systems ◽  
Mutation Rates ◽  
Small Probability ◽  
Deterministic Systems ◽  
Simple Rules ◽  
Computational Work ◽  
A Cell ◽  
Key Topics

Cellular automata are discreet mathematical models. They consist of cells; each cell can exist in a limited number of mutually exclusive states, like 0 or 1. The state of each cell at time t is determined by simple rules, based on the states of its neighboring cells. While exploring their relevance to behavioral sciences (McDowell & Popa, 2009) one aspect caught my attention: it seemed that all emerging structures and patterns could be traced back to the first few generations; patterns were evolving, colliding, changing, and disappearing, but no new patterns or structures were emerging from non-patterns (i.e., "uniformity"). In biological systems, novelty is made possible by mutation (Thomas, 1974) – a concept central to my computational work on learning (Popa & McDowell, 2016) and on the emergence of psychological objects and phenomena from changes in neuronal activation states (Popa, 2019). Unlike biological systems, automata are deterministic systems, governed by precise rules. The question examined here was: what if these rules weren’t precise? What if every time a cell is created, there’s a small probability to make a mistake, to write 0 instead of 1 and vice-versa? I explored this idea in the context of Rule 110 (01101110), an elementary CA notorious for its fascinating properties (Wolfram, 2002). A small amount of mutation – e.g., 0.00005 probability to make mistakes – facilitated the emergence of new patterns and structures, disconnected from the initial conditions. Mutation rates of 0.0001 – 0.0005 produced an abundance of irregular, interacting structures. Mutation rates higher than 1% prevented the emergence of discernible patterns, producing instead an amalgam of ill-defined, organic-looking structures. These results suggested that imperfect automata may provide useful insight on the evolution of non-deterministic systems and on the emergence of novelty – two key topics in machine learning and artificial intelligence.

Artificial-Intelligence-Based Service-Oriented Architectures (SOAs) for Crisis Management

2018 ◽  
pp. 79-95
Author(s):  
Konstantinos Domdouzis
Keyword(s):  
Artificial Intelligence ◽  
Crisis Management ◽  
Biological Systems ◽  
Management Systems ◽  
Biologically Inspired ◽  
Service Oriented Architectures ◽  
Service Oriented

The complexity of crisis-related situations requires the use of advanced technological infrastructures. In order to develop such infrastructures, specific architectures need to be applied such as the service-oriented architectures (SOAs). The purpose of this chapter is to indicate how SOAs can be used in modern crisis management systems, such as the ATHENA system. The chapter underlines the need for a detailed study of specific biological systems, such as the human brain's hippocampus which follows the current, intense attempts of improvement of the current artificial-intelligence-based systems and the development of a new area in artificial intelligence. A number of conclusions are drawn on how biologically inspired systems can benefit the development of service-oriented architectures.

Using Cellular Automata to Learn About the Immune System

1998 ◽  
Vol 09 (06) ◽  
pp. 793-799 ◽  
Author(s):  
Rita Maria Zorzenon Dos Santos
Keyword(s):  
Immune System ◽  
Cellular Automata ◽  
Time Evolution ◽  
Collective Behavior ◽  
Complex Dynamics ◽  
Biological Systems ◽  
Immune Repertoire ◽  
Probabilistic Cellular Automata ◽  
Cellular Automata Model ◽  
Simple Systems

Cellular automata are very simple systems that can exhibit complex dynamics on its time evolution. Over the last decade there have been many applications of cellular automata to modeling of biological systems. Those applications have been stimulated by the study of complex systems which has brought many insights into the cooperative and global behavior of the biological systems. Along with this discussion we present two different applications of deterministic and also of probabilistic cellular automata that are used to model the dynamics involved in cooperative and collective behavior of the immune system. In the first example, we use a deterministic cellular automata to model the time evolution of the immune repertoire, as a network, according to the Jerne's theory. Using this model we could reproduce some recent experimental results about immunization and aging of the immune system. In the second example, we use a probabilistic cellular automata model to study the evolution of HIV infection and the onset of AIDS. The results are in excellent agreement with experimental data obtained from infected patients. Besides the examples above, other interesting applications, such as models for cancer and recurrent epidemics, are being considered in the present framework.

scholarly journals Quo Vadis, Artificial Intelligence?

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Berrar ◽  
Naoyuki Sato ◽  
Alfons Schuster
Keyword(s):  
Artificial Intelligence ◽  
Computer Simulation ◽  
Systems Biology ◽  
Genetic Information ◽  
Biological Systems ◽  
Life Forms ◽  
Complex Interactions ◽  
Long Time ◽  
Quo Vadis ◽  
Biological Organisms

Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles.

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