3,121 research outputs found
When the path is never shortest: a reality check on shortest path biocomputation
Shortest path problems are a touchstone for evaluating the computing
performance and functional range of novel computing substrates. Much has been
published in recent years regarding the use of biocomputers to solve minimal
path problems such as route optimisation and labyrinth navigation, but their
outputs are typically difficult to reproduce and somewhat abstract in nature,
suggesting that both experimental design and analysis in the field require
standardising. This chapter details laboratory experimental data which probe
the path finding process in two single-celled protistic model organisms,
Physarum polycephalum and Paramecium caudatum, comprising a shortest path
problem and labyrinth navigation, respectively. The results presented
illustrate several of the key difficulties that are encountered in categorising
biological behaviours in the language of computing, including biological
variability, non-halting operations and adverse reactions to experimental
stimuli. It is concluded that neither organism examined are able to efficiently
or reproducibly solve shortest path problems in the specific experimental
conditions that were tested. Data presented are contextualised with biological
theory and design principles for maximising the usefulness of experimental
biocomputer prototypes.Comment: To appear in: Adamatzky, A (Ed.) Shortest path solvers. From software
to wetware. Springer, 201
Trajectories entropy in dynamical graphs with memory
In this paper we investigate the application of non-local graph entropy to
evolving and dynamical graphs. The measure is based upon the notion of Markov
diffusion on a graph, and relies on the entropy applied to trajectories
originating at a specific node. In particular, we study the model of
reinforcement-decay graph dynamics, which leads to scale free graphs. We find
that the node entropy characterizes the structure of the network in the two
parameter phase-space describing the dynamical evolution of the weighted graph.
We then apply an adapted version of the entropy measure to purely memristive
circuits. We provide evidence that meanwhile in the case of DC voltage the
entropy based on the forward probability is enough to characterize the graph
properties, in the case of AC voltage generators one needs to consider both
forward and backward based transition probabilities. We provide also evidence
that the entropy highlights the self-organizing properties of memristive
circuits, which re-organizes itself to satisfy the symmetries of the underlying
graph.Comment: 15 pages one column, 10 figures; new analysis and memristor models
added. Text improve
A Stochastic Approach to Shortcut Bridging in Programmable Matter
In a self-organizing particle system, an abstraction of programmable matter,
simple computational elements called particles with limited memory and
communication self-organize to solve system-wide problems of movement,
coordination, and configuration. In this paper, we consider a stochastic,
distributed, local, asynchronous algorithm for "shortcut bridging", in which
particles self-assemble bridges over gaps that simultaneously balance
minimizing the length and cost of the bridge. Army ants of the genus Eciton
have been observed exhibiting a similar behavior in their foraging trails,
dynamically adjusting their bridges to satisfy an efficiency trade-off using
local interactions. Using techniques from Markov chain analysis, we rigorously
analyze our algorithm, show it achieves a near-optimal balance between the
competing factors of path length and bridge cost, and prove that it exhibits a
dependence on the angle of the gap being "shortcut" similar to that of the ant
bridges. We also present simulation results that qualitatively compare our
algorithm with the army ant bridging behavior. Our work gives a plausible
explanation of how convergence to globally optimal configurations can be
achieved via local interactions by simple organisms (e.g., ants) with some
limited computational power and access to random bits. The proposed algorithm
also demonstrates the robustness of the stochastic approach to algorithms for
programmable matter, as it is a surprisingly simple extension of our previous
stochastic algorithm for compression.Comment: Published in Proc. of DNA23: DNA Computing and Molecular Programming
- 23rd International Conference, 2017. An updated journal version will appear
in the DNA23 Special Issue of Natural Computin
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