This page describes the organizational forces that limit change. It explains how to overcome them when necessary.
This page uses an example to illustrate how:
This page uses the example of HP's printer organization freeing itself from its organizational constraints to sell a printer targeted at the IBM pc user.
The constraints are described.
The techniques to overcome them are implied.
Perl code examples
SummaryThis page introduces the programs that the Adaptive Web Framework (AWF) develops and uses to deploy Rob's Strategy Studio (RSS).
The programs are structured to obey complex adaptive system (CAS) principles. That allows AWF to experiment and examine the effects.
A production program generates the web pages.
A testing system tests the production program. It uses a framework to support the test programs. This is AWF's agent programming framework as described in the
This presentation applies complex adaptive system (CAS) agents to computer programming.agent-based programming presentation.
An example of the other AWF agent-based programs that are also described in the frame is the virtual robot.
Finally a strength, weaknesses, opportunities and threats assessment is presented.
IntroductionThe pages in this web frame describe Perl is Larry Wall's programming language. It is designed to make easy tasks easy and hard tasks possible. It has powerful text processing features and can interpret a string of text as code. code fragments which implemented a simulation of a biological cell, a relatively large multi-component cell type from which yeast and multi-celled plants and animals, including humans, is constructed. It contains modules including a nucleus and production functions such as mitochondria. architecture, using complex adaptive system (
This page introduces the complex adaptive system (CAS) theory frame. The theory is positioned relative to the natural sciences. It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents.CAS) principles. The biological cell is a low level adaptive system implemented directly by chemical, molecules obtain chemical properties from the atoms from which they are composed and from the environment in which they exist. Being relatively small they are subject to phenomena which move them about, inducing collisions and possibly reactions with other molecules. AWF's Smiley simulates a chemical environment including associating the 'molecule' like strings with codelet based forces that allow the strings to react based on their component parts, sequence etc. and physical
John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
This page discusses the physical foundations of complex adaptive systems (CAS). A small set of rules is obeyed. New [epi]phenomena then emerge. Examples are discussed.phenomena. The web frame pages were developed to bring different aspects of the complex system into focus. A strengths, weaknesses, opportunities and threats table (SWOT) illustrates the potential and challenges of the CAS approach.
The simulated cell architecture has a number of interacting parts.
eukaryotic, a relatively large multi-component cell type from which yeast and multi-celled plants and animals, including humans, is constructed. It contains modules including a nucleus and production functions such as mitochondria. Cell's organelles such as mitochondria are the energy molecule generating production functions of eukaryotic cells. They are vestigial blue-green bacteria with their own DNA and infrastructure. Unlike stand-alone bacteria they also use the eukaryotic host DNA and infrastructure for some functions. The high energy molecules are nucleotides with a high energy phosphate bond. The most used high energy molecule is Adenosine-tri-phosphate. and chloroplasts are the light energy capturing production functions of eukaryotic plant cells. They are vestigial blue-green bacteria with their own DNA and infrastructure. .
Supporting and controlling the
Flows of different kinds are essential to the operation of complex adaptive systems (CAS).flows in and out of the production system is a super-structure. Both the super-structure and production functions are subject to the control of
Example flows are outlined. Constraints on flows support the emergence of the systems. Examples of constraints are discussed.
Plans change in complex adaptive systems (CAS) due to the action of genetic operations such as mutation, splitting and recombination. The nature of the operations is described.genetic operators and selection pressure from a testing system.
One aspect of the production system is a separation of process & operation flows based on Shigeo Shingo's Toyota study. It turns out that the nature of the transaction is an operation which guarantees to complete a defined set of activities or return to the initial state. For a fee the postal service will ensure that a parcel is delivered to its recipient or will return the parcel to the sender. To provide the service it may have to undo the act of trying to deliver the parcel with a compensating action. Since the parcel could be lost or destroyed the service may have to return an equivalent value to the sender.
hugely impacts the costs and benefits of techniques that can be applied to the processes and operations. It is no surprise that optimizations of CAS transactions, such as occur in health care, have generally increased system costs and undermined effectiveness.
This page looks at schematic structures and their uses. It discusses a number of examples:strategy perspective the
As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan.
Finally it includes a section presenting our formal representation of schematic goals.
Each goal has a series of associated complex adaptive system (CAS) strategy strings.
These goals plus strings are detailed for various chess and business examples.
This page describes the Adaptive Web framework (AWF) test system and the agent programming framework (Smiley) that supports its operation.testing process is more interesting with an
Example test system statements are included. To begin a test a test statement is loaded into Smiley while Smiley executes on the Perl interpreter.
Part of Smiley's Perl code focused on setting up the infrastructure is included bellow.
The setup includes:
Rather than oppose the direct thrust of some environmental flow agents can improve their effectiveness with indirect responses. This page explains how agents are architected to do this and discusses some examples of how it can be done.indirect approach to specification, specialization and control. It aims to provide a feedback loop for the genetic operators. The selection pressure applied to the
Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions. The component architecture and structure of the plans is reviewed.schema population used in developing the test systems will encourage the application of the genetic operations. Assertions is a hypothesis which can be tested and found to be true or false. In the adaptive web framework's (AWF) Smiley assertion statements are used to define the test that will be applied by the application's codelets. The statements must include schematic strings which can group complete and become associated with codelets. Smileys own codelets: Coderack generated part and statement enforces the syntax of the assertion. The specific form of the statements is defined in the application's Meta file. Statement codelets also support the operation of the application's Shewhart cycle.
are tested against operational flows with expected and actual results compared using codelets (and
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS). Key research is reviewed.emergent
Plans are interpreted and implemented by agents. This page discusses the properties of agents in a complex adaptive system (CAS).agents aggregated from codelets including: development,
It then presents examples of agents in different CAS. The examples include a computer program where modeling and actions are performed by software agents. These software agents are aggregates.
The participation of agents in flows is introduced and some implications of this are outlined.
This page discusses how a Smiley based application the event processor test program's operational phase is structured.merge streams;) operating on a
The goals of the event processor test application are described.
The implementation strategy is outlined.
Synchronization of Smiley setup completion and operation phase initiation is discussed.
The association of structural Workspaces for state representation is discussed.
An application specific codelet merge streams assert responds to the nature of the assertion. It does not have an emergent structure. Instead it reflects software engineering practice. It includes:
Schematic synchronization of parallel codelet cascades is performed structurally.
The assert merge operon cascade is included.
The Slipnet concept network for merge streams is included.
The codelets and supporting functions are included.
This page describes the Copycat Coderack.Coderack, conforming to Hofstadter & Mitchell's Copycat
The details of the codelet architecture are described.
The specialized use of the Coderack by the adaptive web framework's (AWF) Smiley is discussed.
The codelet scheduling mechanism is discussed.
A variety of Smiley extensions to the Coderack are reviewed.
The Coderack infrastructure functions are included.
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS). Hofstadter and Mitchell's research with Copycat is reviewed.perception & representation architecture.
Copycat's Coderack helps the codelets to dynamically adapt concepts from its
This page describes the Copycat Slipnet.Slipnet, essentially a representation of the physical and chemical relations, to the current context represented as local state in a
The goal of the Slipnet is reviewed.
Smiley's specialized use of the Slipnet is introduced.
The initial Slipnet network used by the 'Merge Streams' and 'Virtual Robot' agent-based applications is setup in initchemistry and is included.
The Slipnet infrastructure and initialization functions are included.
This page describes the Copycat Workspace.Workspace.
The specialized use of the Workspace by the adaptive web framework's (AWF) Smiley is discussed.
How text and XML are imported into the Smiley Workspace is described.
Telomeric aging of schematic structures is introduced.
The internal data structure used to represent the state of each workspace object is included.
The Workspace infrastructure functions are included.
This page discusses a complex adaptive system (CAS) implementation of a genetic algorithm (GA), Melanie Mitchell's robot-janitor built as a set of Copycat codelets integrated using agent-based programming. The improvement in the operation of the robots over succeeding generations of applying the GA is graphed.Mitchell's robot-janitor, using the dog breading repeatedly applies the recombination genetic operation to generate variation and then applies selection. There is little possibility of mutation contributing to the variation but the rich tool bag of alleles present in the breeding population is leveraged in generating 'new' characteristics. like schematic recombination of Holland's genetic algorithm. In this implementation model receptor, in biological cells these proteins are able to span the cell membrane and present an active site which is tailored to interact with a specific signal. When the receptor pairs with its signal, its overall shape changes resulting in changes in the part internal to the cell which can be relayed by the cells signalling infrastructure. In neuron synapses one type of receptor (fast) is associated with an ion channel. The other (slow) is associated with a signalling enzyme chain and modulates the neuron's response.
The CAS that generated, and operated the robot is reviewed, including the implementation details and codelet operational program flow, and the challenges and limitations of this implementation.
The schematic strings which make up the robot's genotype, as well as the signals which are sent to the nucleus of the robot's agents so that the agents can deploy the appropriate response strings (which activate codelets) are listed. The Slipnet configuration required by the system to associate the schematic strings with programmatic forces (codelets) is also listed. The codelets and supporting perl are also listed.
In the conclusion the limitations of the robot-janitor abstraction in studying emergence and creative evolution are discussed and alternative experimental frameworks are proposed. One such, the schematic cell is the subject of a separate page in this web frame.
codelets are schematically associated with various action codelets. The
Plans change in complex adaptive systems (CAS) due to the action of genetic operations such as mutation, splitting and recombination. The nature of the operations is described.genetic algorithm alters the associations to create a new population of robots. The presence of schemata allows the codelet aggregates to be extended to enable emergent exploration of the robot's environment.
Strengths, Weaknesses, Opportunities and Threats (
Complex adaptive systems (
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