Biologically inspired computing
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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 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. 
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. 
Introduction
Using biology's computational model as the future of computing is proposed to:
Today the Internet engineering task force (IETF, the internet engineering task force controls the processes that manage the architecture of the internet.  ) process and the Internet show how effective the parallel computing model can be.  The IETF's request for comment (RFC) specification process is highly analogous to
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 RFC
s.  The fitness of each new generation of protocol specification is tested by deployment through multiple parallel implementations.  When these are found to operate satisfactorily, and the RFC and deployed systems are accepted as being useful, the RFC becomes a standard track protocol and becomes represented widely in Internet infrastructure. 

The set of networking services and protocols that are covered by the IETF's open RFC process have grown broadly as the early platform
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. 
niche expansion
.  

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 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. 
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
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
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:
  • Loading the 'Meta file' specification,
  • Initializing the Slipnet, and Workspaces and loading them
  • So that the Coderack can be called. 
The Coderack, which is the focus of a separate page of the Perl frame then schedules and runs the codelets that are invoked by the test statement structures. 
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. 
.  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 are
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
and adaptive 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 architecture are reviewed by Melanie Mitchell. 
with:

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.  Samuel modeling is described as an approach. 
learning
.  it would include:
Each of these aspects turns out to be complex. 
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


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 include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals
data structures introduces the possibility of supporting human
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
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. 
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. 
exploration of the adjacent possible


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. 
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
,
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
,
This page looks at schematic structures and their uses.  It discusses a number of examples:
  • 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. 
operational plans
,
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 page describes the SWOT process.  That includes:
  • 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. 
Operational SWOT matrices and clusters from the Adaptive Web Framework (AWF) are included as examples. 
SWOT
, and
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


Web frames
As a start, in line with our understanding of cognitive 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:
  • Loading the 'Meta file' specification,
  • Initializing the Slipnet, and Workspaces and loading them
  • So that the Coderack can be called. 
The Coderack, which is the focus of a separate page of the Perl frame then schedules and runs the codelets that are invoked by the test statement structures. 
adaptive test framework
extends the processing of events to adapting to them with agents which emerge from schematic codelet aggregates. 

Automated genetic algorithms provide a vision for increasing the 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. 
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
,
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
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. 

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, 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. 
There seems to be much potential to gain from biologically inspired computing.  But the difficulty of identifying the operations of highly parallel and adaptive 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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This page looks at schematic structures and their uses.  It discusses a number of examples:
  • 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. 
Strategy
| Design |
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. 
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