Enzymatic numerical P systems - a new class of membrane computing systems

Pavel, Ana B.; Arsene, Octavian; Buiu, Cătălin

doi:10.1109/bicta.2010.5645071

Cited by 50 publications

(32 citation statements)

References 3 publications

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“…36 I do not want to play with words any more, I only want to make a comparison: on one hand we have somewhat opaque claims 1 and 2 referring to the phenomenon of self-relatedness for improving machines (hence intelligence), on the other hand, there are analogous and somewhat clearer claims 3 and 4 about amplifiers with positive feedback. In the amplifier a quantity can obtain a higher value, and-thanks to a positive feedback-in the same amplifier the quantity can obtain even higher value.…”

Section: How the Explosion Is Bornmentioning

confidence: 99%

Beyond Artificial Intelligence

Popov¹

2013

Topics in Intelligent Engineering and Informatics

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Abstract. Artificial intelligent systems capable of learning, setting goals, solving problems, finding new solutions and unforeseen behavior scenarios without external assistance exist in a variety of odd models, each using different sets of assumptions and each having its own limitations. The author argues whether the modern artificial intelligent systems can be called truly intelligent so that to be able to realize the vast capabilities of the human brain. In order to generalize core issues of the main artificial intelligence domains such as fuzzy logics, probabilistic reasoning, bio-inspired techniques, neural networks apparatus, and neuroscience advances together with the new areas of chaos theory and nonlinear dynamics, a multidisciplinary analysis has been employed in the work. In the analysis, the evolution of a particular mathematical apparatus is being considered to justify the application of dynamic models with unstable dynamics used to solve intelligent problems of the next generation. The conclusion is soundly based on the idea that the future of artificial intelligence lies in the sphere of nonlinear dynamics and chaos that is absolutely critical to understanding and modeling cognition processes. IntroductionThis chapter analyses the evolution of artificial intelligence (AI) paradigms and the transformational role of multidisciplinary research. The leading role of multidisciplinary knowledge is the key to attaining a qualitative leap in AI. Still it is a matter of future investigations to develop the really true human intelligence although a lot of promising results in different AI areas have already been published [14,27,38]. A great variety of successful AI applications have found their places in the chapters of this book. This chapter aims to share author's vision on intelligent systems development through accumulated experience in the area. In order to reach this aim we

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Section: How the Explosion Is Bornmentioning

confidence: 99%

Beyond Artificial Intelligence

Popov¹

2013

Topics in Intelligent Engineering and Informatics

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“…Thus a selection mechanism for the active rules is defined in the ENPS model [7]. The NPS model with multiple rules per membrane is a non-deterministic system.…”

Section: Nps Modelmentioning

confidence: 99%

Robot Localization Implemented with Enzymatic Numerical P Systems

Pavel

Vasile

Dumitrache

2012

Biomimetic and Biohybrid Systems

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Membrane computing is an interdisciplinary research field focused on new computational models, also known as P systems, inspired by the compartmental model of the cell and the membrane transport mechanisms. Numerical P systems are a type of P systems introduced by Gh. Pȃun in 2006 for possible applications in economics. Recently, an extension of numerical P systems, enzymatic numerical P systems, has been defined in the context of robot control. This paper presents a new approach to modeling and implementing autonomous mobile robot behaviors and proposes a new odometry module implemented with enzymatic numerical P systems for robot localization. The advantages of modeling robot behaviors with enzymatic membrane controllers and the experimental results obtained on real and simulated robots are also discussed.

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“…The idea was introduced in numerical P systems in [12], under the name of enzymatic control: a distinguished variable, called enzyme, is associated with each program and the program is applied only if the current value of the enzyme is not smaller than the smallest value of the variables involved in the production function of the program. The "enzymatic control" is useful in designing robot controllers based on numerical P systems [13][14][15].…”

confidence: 99%

“…The values assumed by a distinguished variable during a computation form the set of numbers computed by the system. Many computational properties of numerical P systems have been investigated at both the theoretical level and at the application level [4,9,10,[12][13][14][15][16][17]. Several strategies of using productionrepartition programs were considered: sequential (at each step, in each region, only one program…”

Section: Introductionmentioning

confidence: 99%

Numerical P Systems with Thresholds

Zhang

Pan

2016

INT J COMPUT COMMUN

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Numerical P systems are a class of P systems inspired both from the structure of living cells and from economics. In this work, a control of using evolution programs is introduced into numerical P systems: a threshold is considered and a program can be applied only when the values of the variables involved in the production function of the program are greater than/equal to (lower-threshold) or smaller than/equal to (upper-threshold) the threshold. The computational power of numerical P systems with lower-threshold or upper-threshold is investigated. It is proved that numerical P systems with a lower-threshold, with one membrane and linear production functions, working both in the all-parallel mode and in the one-parallel mode are universal. The result is also extended to numerical P systems with an upperthreshold, by proving the equivalence of the numerical P systems with lower-and upper-thresholds.

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Enzymatic numerical P systems - a new class of membrane computing systems

Cited by 50 publications

References 3 publications

Beyond Artificial Intelligence

Beyond Artificial Intelligence

Robot Localization Implemented with Enzymatic Numerical P Systems

Numerical P Systems with Thresholds

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