Dynamic Self-Assembly and Computation: From Biological to Information Systems

Abstract. We present two ways in which dynamic self-assembly can be used to perform computation, via stochastic protein networks and self-assembling software. We describe our protein-emulating agent-based simulation infrastructure, which is used for both types of computations, and the few agent properties sufficient for dynamic self-assembly. Examples of protein-networkbased computation and self-assembling software are presented. We describe some novel capabilities that are enabled by the inherently dynamic nature of the self-assembling executable code

Towards an Approach to Overcome Software Brittleness

Development of bug-free, high-surety, complex software is quite difficult using current tools. The brittle nature of the programming constructs in popular languages such as C/C++ is one root cause. Brittle commands force the designer to rigidly specify the minutiae of tasks, e.g. using ''for(index=0;index>total;index++)'', rather than specifying the goals or intent of the tasks, e.g. ''ensure that all relevant data elements have been examined''. Specification of task minutiae makes code hard to comprehend, which in turn encourages design errors/limitations and makes future modifications quite difficult. This report describes an LDRD project to seed the development of a surety computer language, for stand-alone computing environments, to be implemented using the swarm intelligence of autonomous agents. The long term vision of this project was to develop a language with the following surety capabilities: (1) Reliability -- Autonomous agents can appropriate y decide when to act and when a task is complete, provide a natural means for avoiding brittle task specifications, and can overcome many hardware glitches. (2) Safety, security -- Watchdog safety and security agents can monitor other agents to prevent unauthorized or dangerous actions. (3) An immune system -- The small chunks of agent code can have an encryption scheme to enable detection and elimination of unauthorized and corrupted agents. This report describes the progress achieved during this small 9 month project and describes lessons learned

Some Provable Properties of VERI Clustering

We present mathematical proofs for two useful properties of the clusters generated by the visual empirical region of influence (VERI) shape. The first proof shows that, for any d-dimensional vector set with more than one distinct vector, that there exists a bounded spherical volume about each vector v which contains all of the vectors that can VERI cluster with v, and that the radius of this d-dimensional volume scales linearly with the nearest neighbor distance to v. We then prove, using only each vector's nearest neighbor as an inhibitor, that there is a single upper bound on the number of VERI clusterings for each vector in any d-dimensional vector set, provided that there are no duplicate vectors. These proofs guarantee significant improvement in VERI algorithm runtimes over the brute force O(N{sup 3}) implementation required for general d-dimensional region of influence implementations and indicate a method for improving approximate O(NlogN) VERI implementations. We also present a related region of influence shape called the VERI bow tie that has been recently used in certain swam intelligence algorithms. We prove that the VERI bow tie produces connected graphs for arbitrary d-dimensional data sets (if the bow tie boundary line is not included in the region of influence). We then prove that the VERI bow tie also produces a bounded number of clusterings for each vector in any d-dimensional vector set, provided that there are no duplicate vectors (and the bow tie boundary line is included in the region of influence)

Self Organization of Software LDRD Final Report

We are currently exploring and developing a new statistical mechanics approach to designing self organizing and self assembling systems that is unique to SNL. The primary application target for this ongoing research is the development of new kinds of nanoscale components and hardware systems. However, a surprising out of the box connection to software development is emerging from this effort. With some amount of modification, the collective behavior physics ideas for enabling simple hardware components to self organize may also provide design methods for a new class of software modules. Large numbers of these relatively small software components, if designed correctly, would be able to self assemble into a variety of much larger and more complex software systems. This self organization process would be steered to yield desired sets of system properties. If successful, this would provide a radical (disruptive technology) path to developing complex, high reliability software unlike any known today. The special work needed to evaluate this high risk, high payoff opportunity does not fit well into existing SNL funding categories, as it is well outside of the mainstreams of both conventional software development practices and the nanoscience research area that spawned it. We proposed a small LDRD effort aimed at appropriately generalizing these collective behavior physics concepts and testing their feasibility for achieving the self organization of large software systems. Our favorable results motivate an expanded effort to fully develop self-organizing software as a new technology

Some Novel Design Principles for Collective Behaviors in Mobile Robots

We present a set of novel design principles to aid in the development of complex collective behaviors in fleets of mobile robots. The key elements are: the use of a graph algorithm that we have created, with certain proven properties, that guarantee scalable local communications for fleets of arbitrary size; the use of artificial forces to simplify the design of motion control; the use of certain proximity values in the graph algorithm to simplify the sharing of robust navigation and sensor information among the robots. We describe these design elements and present a computer simulation that illustrates the behaviors readily achievable with these design tools

Versatile Materials for use as Chemically Sensitive

Interfaces in SAW-based Sensor Arrays colvF-q 067 3 --A ABSTRACT The primary research objective of the work described here is to design, synthesize, and characterize new materials for use as chemical sensor interfaces, integrate these materials, using appropriate transducers, into sensor arrays, and then develop appropriate mathematical algorithms for interpreting the array response. In this paper, we will discuss two new types of materials we have developed that are ideally suited for use as chemically sensitive interfaces for array-based chemical sensing applications, since they: (1) provide general specificity towards classes of functional groups rather than individual compounds; (2) are intermediate in structure between monolayers and polymers; (3) exhibit both endo-and exo-recognition. The fust class of materials is surface-confined dendrimers and the second is hyperbranched polymers

Detection of Volatile Organics Using a Surface Acoustic Wave Array System

A chemical sensing system based on arrays of surface acoustic wave (SAW) delay lines has been developed for identification and quantification of volatile organic compounds (VOCs). The individual SAW chemical sensors consist of interdigital transducers patterned on the surface of an ST-cut quartz substrate to launch and detect the acoustic waves and a thin film coating in the SAW propagation path to perturb the acoustic wave velocity and attenuation during analyte sorption. A diverse set of material coatings gives the sensor arrays a degree of chemical sensitivity and selectivity. Materials examined for sensor application include the alkanethiol-based self-assembled monolayer, plasma-processed films, custom-synthesized conventional polymers, dendrimeric polymers, molecular recognition materials, electroplated metal thin films, and porous metal oxides. All of these materials target a specific chemical fi.mctionality and the enhancement of accessible film surface area. Since no one coating provides absolute analyte specificity, the array responses are further analyzed using a visual-empirical region-of-influence (VERI) pattern recognition algorithm. The chemical sensing system consists of a seven-element SAW array with accompanying drive and control electronics, sensor signal acquisition electronics, environmental vapor sampling hardware, and a notebook computer. Based on data gathered for individual sensor responses, greater than 93%-accurate identification can be achieved for any single analyte from a group of 17 VOCs and water