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:
- A business can gain focus from targeting key customers,
- Business planning activities performed by the whole organization can build awareness, empowerment and coherence.
- A program approach can ensure strategic alignment.
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.
The constraints are described.
The techniques to overcome them are implied.
Potential of biologically inspired computing
Summary
This web page reviews opportunities to enhance computing theory and practice by using biological mechanisms and complex adaptive system ( This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
CAS) theory. John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
Introduction
Using biology's computational model as the future of computing is proposed to:- Overcome the Von
Neumann fetch compute store, John was a brilliant Hungarian mathematician who published the earliest paper specifying architecture for digital computing. It ensured this computing architecture was not patentable. The architecture has a central processing unit (CPU), random access storage addressable by the CPU and a sequencer. The architecture encourages a serial software architecture that matches the logic of the sequencer and processing operations on program and data. Von Neumann, his history, computing architecture and some alternative architectures are reviewed by Melanie Mitchell.
bottleneck with massively parallel systems. - Increase the robustness of computations by duplication of
processing.
- Improve the To benefit from shifts in the environment agents must be flexible. Being sensitive to environmental signals agents who adjust strategic priorities can constrain their competitors.flexibility of programmed systems by use of an adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage. Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents. Evolution indicates fitness when an organism survives and reproduces. For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery. He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's. When an agent achieved an explicit goal they obtained a final reward. Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas. The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness'; In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary.
architecture.
- Leverage biological
designs which have been optimized over billions of
years.
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 operations on a 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.
schematic database of RFCs. The
fitness is, according to Dawkins, a suitcase word with at least five meanings in biology: - Darwin and Wallace thought in terms of the capacity to survive and reproduce, but they were considering discrete aspects such as chewing grass - where hard enamel would improve the relative fitness.
- Population geneticists: Ronald Fisher, Sewall Wright, J.B.S. Haldane; consider selection at a locus where for a genotype: green eyes vs blue eyes; one with higher fitness can be identified from genotypic frequencies and gene frequencies, with all other variations averaged out.
- Whole organism 'integrated' fitness. Dawkins notes there is only ever one instance of a specific organism. Being unique, comparing the relative success of its offspring makes little sense. Over a huge number of generations the individual is likely to have provided a contribution to everyone in the pool or no one.
- Inclusive fitness, where according to Hamilton, fitness depends on an organism's actions or effects on its children or its relative's children, a model where natural selection favors organs and behaviors that cause the individual's genes to be passed on. It is easy to mistakenly count an offspring in multiple relative's fitness assessments.
- Personal fitness represents the effects a person's relatives have on the individual's fitness [3]. When interpreted correctly fitness [4] and fitness [5] are the same.
The set of networking services and protocols that are covered by the IETF's open RFC process have grown broadly as the early platform is agent generated infrastructure that supports emergence of an entity through: leverage of an abundant energy source, reusable resources; attracting a phenotypically aligned network of agents.
Walter Shewhart's iterative development process is found in many
complex adaptive systems (CAS).
The mechanism is reviewed and its value in coping with random
events is explained.
iterative construction, deployment and
revision cycle, enabled This web page reviews opportunities to find and capture new
niches, based on studying fitness landscapes using complex
adaptive system (CAS) theory.
CAS SuperOrganisms are
able to capture rich niches. A variety of CAS are
included: chess, prokaryotes,
nation states, businesses, economies; along
with change mechanisms: evolution
and artificial
intelligence; agency
effects and environmental impacts.
Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
niche
expansion. Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
The robustness and increased performance of massively parallel computing applications is demonstrated by Google search. Leveraging the opportunity that a networked web of HTML pages offered, Google's capture, analysis and search processes are designed to leverage racks of computers to break up the search problem into parallel activities. But the algorithms are still designed, implemented and operated by software engineers.
Google deploys its map, reduce applications over a distributed storage infrastructure that ensures the applications are close to a copy of the data they are working with. Biological systems use cell division to generate a similar effect. Since complex adaptive system (
This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
CAS)
based John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
Plans are interpreted and implemented by agents. This page
discusses the properties of agents in a complex adaptive system
(CAS).
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.
agents replicate common 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.
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.
schematic data, programs with a CAS
architecture, such as This page describes the Adaptive Web framework (AWF) test system
and the agent programming framework (Smiley) that supports its
operation.
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:
the adaptive test
framework, can be deployed over a distributed storage
infrastructure, such as HADOOP is an open source implementation of the 'big data' distributed file system architecture used by Google to support its map-reduce programs. The map-reduce programs construct associations between vast numbers of web pages and typical search terms. 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:
- Loading the 'Meta file'
specification,
- Initializing the Slipnet,
and Workspaces and loading them
- So that the Coderack can be called.
. HADOOP can then be leveraged to help ensure that only light weight
Agents use sensors to detect events in their environment.
This page reviews how these events become signals associated
with beneficial responses in a complex adaptive system (CAS). CAS signals emerge from
the Darwinian information model. Signals can indicate decision summaries and level of
uncertainty.
signals need be sent between
the computers on which the agents are distributed. The general computing paradigm does not reflect biology in its architecture. While the business activities supporting product development is a phase during the operation of a CAS agent. It allows for schematic strategies to be iteratively blended with environmental signals to solve the logistical issues of migrating newly built and transformed sub-agents. That is needed to achieve the adult configuration of the agent and optimize it for the proximate environment. Smiley includes examples of the developmental phase agents required in an emergent CAS. In situations where parents invest in the growth and memetic learning of their offspring the schematic grab bag can support optimizations to develop models, structures and actions to construct an adept adult. In humans, adolescence leverages neural plasticity, elder sibling advice and adult coaching to help prepare the deploying neuronal network and body to successfully compete. are
This page discusses the mechanisms and effects of emergence
underpinning any complex adaptive system (CAS). Physical forces and
constraints follow the rules of complexity. They generate
phenomena and support the indirect emergence of epiphenomena.
Flows of epiphenomena interact in events which support the
emergence of equilibrium and autonomous
entities. Autonomous entities enable evolution
to operate broadening the adjacent possible.
Key research is reviewed.
emergent and adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage. Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents. Evolution indicates fitness when an organism survives and reproduces. For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery. He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's. When an agent achieved an explicit goal they obtained a final reward. Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas. The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness'; In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary. the application paradigm is not. Google not withstanding the general approach still reflects Von Neumann's arguments, John was a brilliant Hungarian mathematician who published the earliest paper specifying architecture for digital computing. It ensured this computing architecture was not patentable. The architecture has a central processing unit (CPU), random access storage addressable by the CPU and a sequencer. The architecture encourages a serial software architecture that matches the logic of the sequencer and processing operations on program and data. Von Neumann, his history, computing architecture and some alternative architectures are reviewed by Melanie Mitchell.
with:
- A central processing unit (CPU) resource supplied with
instructions and data to process. Biology uses a
massively distributed processing model which reduces the CPU
access bottleneck. Consequently biological operations
do not have to limit competitive invocations, and are less
likely to need transactional 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.
protection.
- Instructions processed in series, unless a jump is
executed.
- Applications are designed to directly implement specific
functional requirements. Maintenance issues and
engineering quality strategies including limited
connections, reuse, directness and information hiding have
promoted Bertrand Meyer develops arguments, principles and strategies for creating modular software. He concludes that abstract data types and inheritence make object orientation a superior methodology for software construction. Complex adaptive system (CAS) theory suggests agents provide an alternative strategy to the use of objects.object oriented methods and tool chains. Biology uses
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 methods which encourage emergence.
- Testing is applied to the operation reflecting the design
of the modules and the requirements of the system. The
testing process aims to select for This web page reviews opportunities to find and capture new niches, based on studying fitness landscapes using complex adaptive system (CAS) theory. CAS SuperOrganisms are able to capture rich niches. A variety of CAS are included: chess, prokaryotes, nation states, businesses, economies; along with change mechanisms: evolution and artificial intelligence; agency effects and environmental impacts.fitness, filtering instances of the implementation which fail the criteria. This is an
Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS). The mechanism and its emergence are discussed.evolutionary selection process, but it acts directly on the source code instead of the schematic structures, such as the engineer's understandings of the requirements and designs. The
This page describes the Adaptive Web framework (AWF) test system and the agent programming framework (Smiley) that supports its operation.Adaptive web framework (AWF) testing infrastructure (2) explores more biologically aligned alternatives.
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:
- Loading the 'Meta file'
specification,
- Initializing the Slipnet,
and Workspaces and loading them
- So that the Coderack can be called.
- Loading the 'Meta file'
specification,
- The released application then competes in the
marketplace. Finally selection has an impact on the
schematic structures.
The application does not respond flexibly to the situation. It expects specific input 'signals' and uses these to transition between its predetermined set of states. If the application is given unexpected inputs it can only reject them. If its understanding of the real world is flawed when it detects a mismatch at best it can perform error recovery to return to a defined state.
A biologically inspired computing architecture would have improved
To benefit from shifts in the environment agents must be flexible. Being
sensitive to environmental signals
agents who adjust strategic priorities can constrain their
competitors.
flexibility and should
enable adaptive The agents in complex adaptive
systems (CAS) must model their
environment to respond effectively to it. Evolution's
schematic operators and Samuel
modeling together support the indirect recording of past
successes and their strategic use by the current agent to learn
how to succeed in the proximate environment.
learning. it
would include:- Sensors which detect the
real world signals that we sense.
- Models of the world defined in both sensor input form and
higher level multi-modal, real world effects include sound, light, touch etc. at the same time.
forms
- Networks of agents with a shared state that allows them to respond to the signals and initiate further signals and actions.
- Learned associations of localized signals with general aspects that are important to detect and respond to.
- An emergent
architecture supporting adaptive coordinated
deployment of the network of agents.
Biological sensors
Real world sensors have to detect relatively low level aspects of the physical world. The input signals are typically multi-modal, real world effects include sound, light, touch etc. at the same time.so multiple
Agents use sensors to detect events in their environment.
This page reviews how these events become signals associated
with beneficial responses in a complex adaptive system (CAS). CAS signals emerge from
the Darwinian information model. Signals can indicate decision summaries and level of
uncertainty.
sensors will detect aspects of a
signal. Physiological psychologists have studied
how animals perform this type of sensing. Vast
collections of networked sensors deployed peripherally detect
the low level aspects of each signal. Detected signals are
flowed through layers of a network where coincidences are
identified and used to associate the aspects of the signals with
potential higher level effects of the real world. At the
highest levels these associations have been integrated into a
multi-mode association which is related to the animals
understanding of its own situation and that of its immediate The complex adaptive system (CAS)
nature of a value delivery system is first introduced. It's a network
of agents acting as relays.
The critical nature of hub agents and the difficulty of altering an aligned network is reviewed.
The nature of and exceptional opportunities created by platforms are discussed.
Finally an example of aligning a VDS is presented.
environment. The critical nature of hub agents and the difficulty of altering an aligned network is reviewed.
The nature of and exceptional opportunities created by platforms are discussed.
Finally an example of aligning a VDS is presented.
Peripheral sensors can include
Representing state in emergent entities is essential but
difficult. Various structures are used to enhance the rate
and scope of state transitions. Examples are
discussed.
mechanisms
to: - Resolve basic aspects of the signal locally so that only highly significant attributes are passed on to other agents in the network.
- Align detector deployment with local aspects of the
environment.
Use of biological structures and procedures to improve the fitness of designs
The development of 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.
schematic structures
in the form of genetic and neuronal, specialized eukaryotic cells include channels which control flows of sodium and potassium ions across the massively extended cell membrane supporting an electro-chemical wave which is then converted into an outgoing chemical signal transmission from synapses which target nearby neuron or muscle cell receptors. Neurons are supported by glial cells. Neurons include a: - Receptive element - dendrites
- Transmitting element - axon and synaptic terminals. The axon may be myelinated, focusing the signals through synaptic transmission, or unmyelinated - where crosstalk is leveraged.
- Highly variable DNA schema using transposons.
Plans are interpreted and implemented by agents. This page
discusses the properties of agents in a complex adaptive system
(CAS).
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.
agents with 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.
Tools and the businesses that produce them have evolved
dramatically. W Brian Arthur shows how this occurred.
tools
that utilize schematic This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed. The bridging of a node from a network of 'well
known' percepts to a new representational instance is discussed
as it occurs in biochemistry, in consciousness and
abstractly.
representations
and support perceptions and This web page reviews opportunities to find and capture new
niches, based on studying fitness landscapes using complex
adaptive system (CAS) theory.
CAS SuperOrganisms are
able to capture rich niches. A variety of CAS are
included: chess, prokaryotes,
nation states, businesses, economies; along
with change mechanisms: evolution
and artificial
intelligence; agency
effects and environmental impacts.
Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
exploration
of the adjacent possible. Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
Already wikis provide support for the shared development of germ-line, a master copy of the schematic structures is maintained for reproduction of offspring. There will also be somatic copies which are modified by the operational agents so that they can represent their current state. representations - most notably wikipedia. The
Walter Shewhart's iterative development process is found in many
complex adaptive systems (CAS).
The mechanism is reviewed and its value in coping with random
events is explained.
iterative updating of this data is an
area of focus for the wiki community, but it does not include
use of This page reviews the implications of reproduction initially
generating a single child cell. The mechanism and
resulting strategic options are discussed.
a single cell developmental
bottleneck for initializing the distributed somatic, Schematic structures which are used to support the operation of the agent. They are modified as the agent's state changes unlike the germ-line schemata. state.
Potentially the somatic This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed. The bridging of a node from a network of 'well
known' percepts to a new representational instance is discussed
as it occurs in biochemistry, in consciousness and
abstractly.
representation,
could include perceptions of the: active This page looks at how scenarios allow people to relate to the
possible evolution of the business and its products and
services. The Long view process is highlighted.
Value based customer segmentation is reviewed. Keirsey's psychological categorization and 'crossing the chasm' are highlighted.
Three alternate systems are framed as long view scenarios (1) development of a billing mediation business, (2) development of the Grameen Bank the first micro loan bank and (3) some classic chess games.
Some of the scenarios will be referenced in the SWOT and planning pages of this frame. In particular the complex adaptive system (CAS) goals used will be referenced by the planning pages schemetic goals.
situation, Value based customer segmentation is reviewed. Keirsey's psychological categorization and 'crossing the chasm' are highlighted.
Three alternate systems are framed as long view scenarios (1) development of a billing mediation business, (2) development of the Grameen Bank the first micro loan bank and (3) some classic chess games.
Some of the scenarios will be referenced in the SWOT and planning pages of this frame. In particular the complex adaptive system (CAS) goals used will be referenced by the planning pages schemetic goals.
First describes the dynamic
nature of any complex adaptive system (CAS).
It then introduces the broad effects of change which includes opportunities and risks/uncertainties.
As a CAS grows opportunities become undermined so they must be acted on quickly.
Uncertainties are also transformed and relayed by the dynamic network. In particular the recombination of current and new ideas brought in from the network is discussed.
target
niches, It then introduces the broad effects of change which includes opportunities and risks/uncertainties.
As a CAS grows opportunities become undermined so they must be acted on quickly.
Uncertainties are also transformed and relayed by the dynamic network. In particular the recombination of current and new ideas brought in from the network is discussed.
This page looks at schematic structures
and their uses. It discusses a number of examples:
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.
operational plans, - Schematic ideas are recombined in creativity.
- Similarly designers take ideas and rules about materials and components and combine them.
- Schematic Recipes help to standardize operations.
- Modular components are combined into strategies for use in business plans and business models.
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.
The page reviews how complex systems can be analyzed.
The resulting analysis supports evaluation of system events.
The analysis enables categorization of different events into classes.
The analysis helps with recombination of the models to enable creativity.
The page advocates an iterative approach including support from models.
analysis stores and The resulting analysis supports evaluation of system events.
The analysis enables categorization of different events into classes.
The analysis helps with recombination of the models to enable creativity.
The page advocates an iterative approach including support from models.
The page describes the SWOT
process. That includes:
SWOT, and - The classification
of each event into strength weakness opportunity and
threat.
- The clustering
process for grouping the classified events into goals.
- How the clusters
can support planning and execution.
The complex adaptive system (CAS)
nature of a value delivery system is first introduced. It's a network
of agents acting as relays.
The critical nature of hub agents and the difficulty of altering an aligned network is reviewed.
The nature of and exceptional opportunities created by platforms are discussed.
Finally an example of aligning a VDS is presented.
value
delivery system; The critical nature of hub agents and the difficulty of altering an aligned network is reviewed.
The nature of and exceptional opportunities created by platforms are discussed.
Finally an example of aligning a VDS is presented.
Web frames
As a start, in line with our understanding of cognitive is the ability to orchestrate thought and action in accordance with internal goals according to Princeton's Jonathan Cohen.processing, AWF's event processor supports the construction of frames of tailored web pages. Each page of a frame aims to include only information, and links, relevant to the specific focus of the web page. A frame of pages allows different points of focus to be constructed, each linked together to provide the reader with a paced and, hopefully, comprehensive perspective.
Networks
of agents with shared state
Agent-based programming, as instantiated in AWF's This page describes the Adaptive Web framework (AWF) test system
and the agent programming framework (Smiley) that supports its
operation.
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:
adaptive test framework extends the
processing of events to adapting to them with agents which
emerge from schematic codelet
aggregates. 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:
- Loading the 'Meta file'
specification,
- Initializing the Slipnet,
and Workspaces and loading them
- So that the Coderack can be called.
Automated genetic algorithms provide a vision for increasing the
To benefit from shifts in the environment agents must be flexible. Being
sensitive to environmental signals
agents who adjust strategic priorities can constrain their
competitors.
flexibility and creativity
of designed systems. Infrastructure supporting adaptive coordinated deployment of the network of agents
Douglas Hofstadter and Melanie Mitchell's This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed. The bridging of a node from a network of 'well
known' percepts to a new representational instance is discussed
as it occurs in biochemistry, in consciousness and
abstractly.
Copycat
This page describes the Copycat
Coderack.
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.
Coderack, 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 describes the Copycat
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.
Workspace
and 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 describes the Copycat
Slipnet.
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.
Slipnet define the basis of our
emergent architecture for deploying agents. 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.
Hofstadter and Mitchell's Copycat infrastructure needs augmentation with
This page describes the Smiley
infrastructure that supports the associative binding of schematic strings to codelets
defined in the Meta file
and Slipnet.
The infrastructure supporting the associations is introduced.
The role of Jeff Hawkins neocortical attributes is discussed.
Relevant Slipnet configurations are included.
The codelets and supporting functions are included.
associative labeling
of the Workspace objects is a collection of: happenings, occurrences and processes; including emergent entities, as required by relativity, explains Rovelli. But natural selection has improved our fitness by representing this perception, in our minds, as an unchanging thing, as explained by Pinker. Dehaene explains the object modeling and construction process within the unconscious and conscious brain. Mathematicians view anything that can be defined and used in deductive reasoning and mathematical proofs as an object. These mathematical objects can be values of variables, allowing them to be used in formulas. The infrastructure supporting the associations is introduced.
The role of Jeff Hawkins neocortical attributes is discussed.
Relevant Slipnet configurations are included.
The codelets and supporting functions are included.
, but is otherwise essentially able to provide a parallel environment for the codelet based agents.
The infrastructure also needs to operate in parallel to reflect the massively parallel biochemical systems. A start is to support:
- GPU streams within the Copycat infrastructure, and
- Networks of cooperating Copycats.
biological systems and correctly interpreting biological mechanisms has so far resulted in speculative proposals that over simplify the aggregate operation of biological systems.
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Business Physics |
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integrating quality appropriate for each market |
This page looks at schematic structures
and their uses. It discusses a number of examples:
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.
Strategy |
Design |
- Schematic ideas are recombined in creativity.
- Similarly designers take ideas and rules about materials and components and combine them.
- Schematic Recipes help to standardize operations.
- Modular components are combined into strategies for use in business plans and business models.
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 uses an example to illustrate how:
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- A business can gain focus from targeting key customers,
- Business planning activities performed by the whole organization can build awareness, empowerment and coherence.
- A program approach can ensure strategic alignment.
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