Partially asynchronous distributed algorithms for 2-dimensional multi-agent supporting systems

Author(s):  
K. Jin ◽  
M.M. Larrondo-Petrie
Author(s):  
Christan Müller ◽  
Helmut Seidl

AbstractFirst-order transition systems are a convenient formalism to specify parametric systems such as multi-agent workflows or distributed algorithms. In general, any nontrivial question about such systems is undecidable. Here, we present three subclasses of first-order transition systems where every universal invariant can effectively be decided via fixpoint iteration. These subclasses are defined in terms of syntactical restrictions: negation, stratification and guardedness. While guardedness represents a particular pattern how input predicates control existential quantifiers, stratification limits the information flow between predicates. Guardedness implies that the weakest precondition for every universal invariant is again universal, while the remaining sufficient criteria enforce that either the number of first-order variables, or the number of required instances of input predicates remains bounded, or the number of occurring negated literals decreases in every iteration. We argue for each of these three cases that termination of the fixpoint iteration can be guaranteed.


2022 ◽  
Vol 1215 (1) ◽  
pp. 012001
Author(s):  
O.N. Granichin ◽  
O.A. Granichina ◽  
V.A. Erofeeva ◽  
A.V. Leonova ◽  
A.A. Senov

Abstract Emergent intelligence is a property of a system of elements that is not inherent in each element individually. This behavior is based on local communications. This behavior helps to adapt to emerging uncertainties and achieve a global goal. This behavior exists in the natural world. A simplified example of emergent intelligence from the natural world is given. The repetition of natural behavior with the help of simple technical devices, which are limited in resources and cheap in construction, and the use of multi-agent approaches is considered. Distributed algorithms using local communications are considered. Such algorithms are more robust to noise.


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