3 research outputs found
Playing with Derivation Modes and Halting Conditions
In the area of P systems, besides the standard maximally parallel derivation
mode, many other derivation modes have been investigated, too. In this paper, many
variants of hierarchical P systems and tissue P systems using different derivation modes
are considered and the effects of using di erent derivation modes, especially the maximally
parallel derivation modes and the maximally parallel set derivation modes, on the
generative and accepting power are illustrated. Moreover, an overview on some control
mechanisms used for (tissue) P systems is given.
Furthermore, besides the standard total halting mode, we also consider different halting
conditions such as unconditional halting and partial halting and explain how the use
of different halting modes may considerably change the computing power of P systems
and tissue P systems
P Systems: from Anti-Matter to Anti-Rules
The concept of a matter object being annihilated when meeting its corresponding
anti-matter object is taken over for rule labels as objects and anti-rule labels
as the corresponding annihilation counterpart in P systems. In the presence of a corresponding
anti-rule object, annihilation of a rule object happens before the rule that the
rule object represents, can be applied. Applying a rule consumes the corresponding rule
object, but may also produce new rule objects as well as anti-rule objects, too. Computational
completeness in this setting then can be obtained in a one-membrane P system
with non-cooperative rules and rule / anti-rule annihilation rules when using one of the
standard maximally parallel derivation modes as well as any of the maximally parallel
set derivation modes (i.e., non-extendable (multi)sets of rules, (multi)sets with maximal
number of rules, (multi)sets of rules a ecting the maximal number of objects). When
using the sequential derivation mode, at least the computational power of partially blind
register machines is obtained
Extended Spiking Neural P Systems with White Hole Rules
We consider extended spiking neural P systems with the additional possibility
of so-called \white hole rules", which send the complete contents of a neuron to
other neurons, and we show how this extension of the original model allow for easy proofs
of the computational completeness of this variant of extended spiking neural P systems
using only one actor neuron. Using only such white hole rules, we can easily simulate
special variants of Lindenmayer systems