Programming for Swarm CS655 Course Project Weilin Zhong

April CS655 Project Weilin Zhong Swarm Computing Models The developments in micro-fabrication and nanotechnology will enable the inexpensive manufacturing of massive numbers of tiny computing elements It will become possible to build new computing models, which incorporate large numbers of small computing elements Computing Swarm is a collection of large number of small computational members

April CS655 Project Weilin Zhong Swarm Computing Examples Cooperative networks of sensors and actuators Intelligent and responsive environment Smart materials (tiles, paint,…) Multi-agent system Software agents Group of robots Amorphous computing medium

April CS655 Project Weilin Zhong Swarm Computing Models System characteristics: Myriad numbers of elements. Decentralized control. Autonomous members behave with simple rules based on local information, interacting with neighbors within a small radius. Members collaborate to achieve sophisticated global behaviors. Unreliable elements with limited power and resource connected in irregular way. Challenge to traditional programming models. How to achieve complex global behaviors from locally information and interaction without individually programming each member? System must be adaptive and self-organized.

April CS655 Project Weilin Zhong Learn From the Nature Social Insects Nest Building Wasp Nets Ant Forage

April CS655 Project Weilin Zhong Learn From the Nature Biology Systems Cell Embryo Cells in an Embryo is an example of complex system The morphology of organism developed from the embryo is an example of global behaviors.

April CS655 Project Weilin Zhong What We Learn? Swarm Intelligence lies on its interaction network Direct Interaction A member interacts with its neighbors Indirection Interaction -- Stigmergy A member interacts indirectly with other members by changing the common environment they live. For example, ants carrying food back to nest might lay some kind of chemical materials, like pheromone on the path thus attract other ants to follow this path to the food.

April CS655 Project Weilin Zhong Programming for Swarm StarLogo GPL – Growing Point Language

April CS655 Project Weilin Zhong StarLogo A Programming language and environment of decentralized multi-agent systems. StarLogo System consists of Turtles and Patches Turtles are the moving agents ( identified by ID numbers) Patches compose the environment where the turtles live ( a regular coordinated spatial grid) A turtles can change the state of the patches it visit All turtles and patches are running in parallel

April CS655 Project Weilin Zhong StarLogo Data Type Booleans(true, false), Lists, Numbers, and Strings. Variable Building State Variables of turtles and patches Xcor, ycor,color,heading,breed,shown?, pendown? Global Variables turtles own [variables] patches own [variables] Local Variables Let [:j 0] Procedure to procedure-name List of Commands ; no separator needed output something ; exits and returns something end

April CS655 Project Weilin Zhong StarLogo Control Flow if, ifelse, case loop, every, repeat, do [list of commands] forever ignore Primitives Turtle Commands forward, bk, die Patch Commands diffuse, diffuse4, nsum, nsum4 Observer Commands ca, crt, setc, setpc ask-turtles, ask-patches,

April CS655 Project Weilin Zhong StarLogo Ant Forage Model Observe Procedures Setup the nest, foods, generate ants Initial ants and patches Turtle Procedures Look-for-food, find-food, return-to-nest, find-nest Patches Procedures diffuse, evaporate

April CS655 Project Weilin Zhong StarLogo Ant Forage Model

April CS655 Project Weilin Zhong StarLogo Advantage Programmable Modeling Environment for Decentralized Multi-agent System with Massive Parallel support Suitable for biology life simulation, traffic, economic and ecology models Limitations Patches are regular coordinated spatial lattice Patches have limited computational capabilities and weak influence to its neighborhood Problems with the language Limited number of Data Types Parameters only passed by value No patches command center

April CS655 Project Weilin Zhong Growing Point Language Amorphous Computing Medium is a system of irregularly placed, identically programmed, asynchronous, locally interacting computational particles GPL(Growing Point Language) is a language to program amorphous computing medium to generate highly complex and prespecified patterns A CMOS Circuit layout obtained from a small program encoding about 17 computational states

April CS655 Project Weilin Zhong Growing Point Language Amorphous Computing Model contains myriad computing particles which are small, limited memory and resources run asynchronously and communicate within a limited distance irregularly placed in a coordinate free space, without the knowledge of the relative direction of a transmitting neighbour execute initially identical programs with different entry points static with fixed interconnection GPL Domain A set of points, a metric for distances between them, step-size particles are referred to as locations

April CS655 Project Weilin Zhong Growing Point Language Botanical Metaphor - Growing Points and Tropisms Growing Point is a lotus of activity which can propagate its activities to its neighbors A growing point move according to its tropism, which is specified in terms of differences in pheromone levels in its neighborhood A growing point can “deposit” material and “secrete” pheromone when it visits a particle The path of growing point is the collection of particles it have visited, expressing by the materials it has deposited on those particles. Theorem Amorphous media can be programmed to draw any prespecified planar graph

April CS655 Project Weilin Zhong Growing Point Language Primitive Datatypes Materials markers to indicate where growing points have previously visited can be associated with a color can be sensed by a growing points to make growth decision Pheromone exists to guide the movement of growing point radially symmetric about the source and monotonic decreasing in the distance from the source Tropism Expressions

April CS655 Project Weilin Zhong GPL – A-to-B segment (define-growing-point (A-to-B) (material A-material) (size 0) (tropism (ortho+ B-pheromone)) (for-each-step (when ((sensing? B-material) (terminate)) (default (propagate))))) (define-growing-point (B-point) (material B-material) (size 0) (for-each-step (secrete+ 10 B-pheromone))) (color ((B-material) "red") ((A-material) "yellow")) (with-locations (a b) (at a (start-growing-point A-to-B)) (at b (start-growing-point B-point)))

April CS655 Project Weilin Zhong GPL – A-to-B Segment

April CS655 Project Weilin Zhong GPL Advantages Irregular and coordinate free domain Strict locality Identically Programmed member Particles compose the environment Suitable for responsive environment and improved materials Facilitate the analysis of programs

April CS655 Project Weilin Zhong GPL -- Limitations and Improvement Dynamic Problem Limitation Static Particles Static Materials and Pheromone Static Model ---- lack of emergence, can only generated prespecified patterns Improvement Mobile particles Changeable Material labeling and Pheromone Evaporation Model Dynamic and Real-time Model

April CS655 Project Weilin Zhong GPL – Limitation and Improvement Reliability Issues Extremely Friendly Environment (well-hehaved and reliable particles) Reliable Communication System Problems and Solutions Communication Error – Error recoverable encoding, retransmission Unreliable Particles – Neighborhood backups Malicious Particles -- Redundant Computation and agreement in neighborhood

April CS655 Project Weilin Zhong StarLogo and GPL Similarity Decentralized system consisting large number of members Autonomous members achieve complex global behaviors from local interaction Massive Parallel Model Object Oriented Shortcomings No consideration of security and survivability Poor Performance

April CS655 Project Weilin Zhong StarLogo and GPL StarLogo Multi-agent System Mobile agents and static env Regularly discrete and coordinated spatial system Dynamic and adaptive models GPL Amorphous Computing Medium Static particles Irregular coordinated free domain Static Models

Summary