Invited Paper: Asynchronous Deterministic Leader Election in Three-Dimensional Programmable Matter

Wed, 04 Jan 2023 00:00:00 +0000

Cutting Through the Noise to Infer Autonomous System Topology

Mon, 02 May 2022 00:00:00 +0000

Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems

Fri, 29 Apr 2022 00:00:00 +0000

Deadlock and Noise in Self-Organized Aggregation Without Computation

Tue, 09 Nov 2021 00:00:00 +0000

The Canonical Amoebot Model: Algorithms and Concurrency Control

Mon, 04 Oct 2021 00:00:00 +0000

Bio-Inspired Energy Distribution for Programmable Matter

Tue, 05 Jan 2021 00:00:00 +0000

Summary

In short, individual bacterium that are metabolically stressed (lacking in nutrients) catalyze a wave of chemical signaling that inhibits the rest of the biofilm’s nutrient uptake. This allows more nutrients to flow to the stressed bacteria, effectively solving the energy distribution problem. We model energy constraints in the amoebot model by giving each particle a battery for storing energy that it can transfer to its neighbors or spend to perform actions. We assume at least one particle in the system has access to an external energy source from which it can harvest energy. The goal of our energy distribution algorithm is to coordinate the transfer of energy between particles so that all particles meet their actions’ energy demands and at least one particle can actually perform its action within a bounded amount of time. After some initial setup, our Energy-Sharing algorithm works in the following phases repeated by each particle individually:

Communication. Particles propagate “stress” and “inhibition” signals to communicate the energy states of starving particles throughout the system.
Sharing. Particles harvest energy they lack from an external source (if they have access to one) and transfer energy to neighbors that need it.
Usage. Particles that are not inhibited spend their stored energy to perform actions, participating in the collective behavior.

We prove that the Energy-Sharing algorithm allows the particle system to meet its energy demands every ${\cal O}(n)$ asynchronous rounds, where $n$ is the number of particles in the system. A simulation of the Energy-Sharing algorithm can be seen below, corresponding to Figure 2 in the paper. All particles are initially idle, with the exception of the root (with external energy access) shown with a gray/black ring. The setup phase establishes the spanning forest (or tree, in this case) rooted at particle(s) with energy access; a particle’s parent direction is shown as an arc. Since all particles start with empty batteries, stress flags (shown as red rings) quickly propagate throughout the system and inhibit flags soon follow. As energy is harvested by the root and shared throughout the system, some particles (shown with yellow rings) receive sufficient energy to meet the demand for their next action but remain inhibited from using it. This inhibition remains until all stressed particles in the system receive sufficient energy to meet their demands, at which point particles (shown with green rings) can reset their inhibit flags and use their energy. After using energy, these particles may again become stressed and trigger another stage of inhibition.

When communication of the stress/inhibit flags is disabled, the energy distribution strategy in Energy-Sharing fails (just as the bacterial biofilms fail to feed all bacteria when their signal relays are disabled). A simulation of this setting is shown below, corresponding to Figure 3 in the paper. Without the communication phase to inhibit particles from using energy while those that are stressed recharge, particles continuously share any energy they have with their descendants in the spanning forest. Thus, while the leaves of the spanning forest occasionally meet their energy demands(seen as the flickering darker green particles), even after 1000 rounds most particles have still not met their energy demand even once.

As an independent but supporting result, we present the Forest-Prune-Repair algorithm that enables a spanning forest of particles to self-repair in the presence of crash failures so long as the set of non-faulty particles remains connected and there is at least one non-crashed root. We prove that this algorithm stabilizes in a spanning forest rooted at root particles in at most ${\cal O}(m^2)$ asynchronous rounds, where $m$ is the number of particles that are severed from the spanning forest by crash failures.

The Energy-Sharing algorithm relies on an underlying spanning forest structure to communicate its stress/inhibition signals. However, if particles move as they often do in collective behaviors, this disrupts the communication structure. Thus, the Forest-Prune-Repair algorithm can be used as an underlying primitive in Energy-Sharing so that Energy-Sharing can be composed with existing amoebot model algorithms, extending them to consider energy constraints. Below is an example of Energy-Sharing composed with Basic Shape Formation, corresponding to Figure 5 in the paper.

Since Energy-Sharing meets the system’s energy demands every ${\cal O}(n)$ asynchronous rounds and Forest-Prune-Repair repairs any disruptions to the communication structure as particles move, a composed algorithm will not be impeded by the underlying energy distribution primitive, but may add significant overhead to its runtime. However, we observe reasonable performance in practice: for example, since hexagon formation terminates in ${\cal O}(n)$ rounds, our proven bounds suggest that the composed algorithm could terminate in time ${\cal O}(n^2)$ or worse but the graph below demonstrates an overhead that appears asymptotically sublinear. With the addition of more energy roots, the composed algorithm is dramatically faster, approaching the runtime achieved without energy constraints.

As a final, informal experiment, we performed a simulation where energy is used for reproduction, mimicking the bacterial biofilms we were inspired by. In our preliminary experiments, we obtain behavior that is qualitatively similar to the biofilm growth patterns observed by Liu and Prindle et al.; in particular, the use of communication and inhibition leads to an oscillatory growth rate. The simulation below corresponds to Figure 7 in the paper.

Convex Hull Formation for Programmable Matter

Sat, 04 Jan 2020 00:00:00 +0000

A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

Fri, 20 Sep 2019 00:00:00 +0000

Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly

Wed, 24 Jul 2019 00:00:00 +0000

Brief Announcement: A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

Mon, 23 Jul 2018 00:00:00 +0000

Improved Leader Election for Self-Organizing Programmable Matter

Sun, 31 Dec 2017 00:00:00 +0000

A Stochastic Approach to Shortcut Bridging in Programmable Matter

Thu, 24 Aug 2017 00:00:00 +0000

Image of Eciton army ants forming a shortcut bridge is reproduced with permission from Reid et al. (PNAS, 2015).

A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems

Mon, 25 Jul 2016 00:00:00 +0000

SSS 2020 Report 2: Compression of programmable matter. Laurent Feuilloley. Discrete Notes, November 2020.
Opening Paths to Progress with Programmable Materials. Joe Kullman. ASU Now, March 2017. (Also featured in ACM TechNews).
A Creeping Model of Computation. Dick Lipton and Ken Regan. Gödel’s Lost Letter and P=NP, September 2016.

1 | Mohammad Moshtaghi