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The emergence of autonomous agents

Summary
Russ Abbott explores the impact on science of epiphenomena and the emergence of agents. 
Emergence explained
In Russ Abbott's paper 'Emergence explained' he explores how epiphenomena can do real work. 

Reductionism and Functionalism
There are only a small number of fundamental forces that impact our world.  But science has identified laws that appear to hold and can be incorporated into theories that provide good predictions of real world outcomes.   Reductionists claim that any law and theory can be derived from the fundamental laws.  Functionalists in contrast suggest that the 'special' sciences, including evolutionary biology and computer science, study regularities that are not reducible to physics. 

Abbott argues that special sciences' regularities are epiphenomena.  They are described in terms that do not depend on the underlying phenomena from which they emerge.  He asserts that the high level regularity is related by supervenience indicates that a high level property does not exist as and of itself but because lower level properties determine the higher level property, which Abbott views as epiphenomena.  Deacon argues that instead of supervenience relations Bickhard's dynamic processes allow the emergence of high level phenomena. 
to the lower level properties.  The regularities do not depend on new real forces. 

Abbott uses a striking example in explanation.  A Turing machine, a machine specified by mathematician Alan Turing which is the blueprint for the electronic programmable computer.  It consists of an infinite tape on which symbols can be written.  A movable read/write tape head which can move about the tape and write on or read symbols from the tape.  A set of rules that tell the head what to do next. 
, implementable on a computer and described by computability theory, is a general theory that aims to classify the computability of a function from the natural numbers.  Turing machines have been used to classify these functions as computable, or decidable. 
, can be deployed on the API of the glider patterns generated during the normal operation of the game of life, is a cellular automaton defined by John Conway.  Cells of the automata are either on or off.  An infinite two-dimensional orthogonal grid of cells allows each cell to interact with its eight immediate neighbors.  The interactions follow set rules: Any on cell with less than two neighbors which are on turns off.  Any on cell with two or three neighbors remains on.  Any on cell with more than three on neighbors turns off.  Any off cell with exactly three on neighbors turns on.  A human watching the display of the cells executing the game of life rules can see 'gliders' move about the grid. 
.  The game of life operates on a very small number of rules, none of which specify gliders, the glider API, or the rules for Turing machines.  Computability theory is an emergent regularity that does not appear to be reducible to the game of life. 

So Abbott proposes a definition of emergence.  A
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
is emergent if it is conceptualized independently of the platform that implements it.  He goes on to review the implications of his definition:

Abbott notes that a smaller level of emergence than regularities occurs.  Entities are a class including people, families, corporations, hurricanes.  Entities implement abstract designs and are demarcatable by their reduced entropy relative to their components. 

Abbott identifies two types of entity:
  1. At equilibrium entities,
  2. Autonomous entities, which can control how they are affected by outside forces;
Autonomous entities are: far from equilibrium, consume and save energy choosing when to use it.  They can use accessible energy to maintain themselves.  Abbott describes the operation of a hurricane.  He argues it is an autonomous entity that is neither biological nor social.  The component air and water molecules, multiple atoms bonded together.  The physical and chemical phenomena associated with the molecule such as charge, size, shape, and potential energy reflect the constituent atoms, the types of bonds between them and the topology of the bonding.  Charged molecules dissolve in aqueous solutions (water).  Uncharged molecules dissolve in lipid bilayers.   that make up the hurricane are only transiently present.  The energy within the hurricane can interact with the environment around the hurricane. 

The ship value delivery system as an emergent agent
A ship is another example of an autonomous agent.  Abbott writes that like the hurricane the ships components and crew are only transiently present.  Only if the maintenance process is also viewed as part of the agent does the ship clearly persist.

Abbott notes that autonomous entities tend not to supervene over any collection of matter that gives it intellectual leverage. 

Abbott links entities to computer science.  He argues that objects are an encapsulation of a mechanism for assuming and changing states along with the means for acting on that encapsulation mechanism and equate to at equilibrium entities.  He sees agents as objects with an internal thread which makes them autonomous entities. 

Based on these equivalences Abbott suggests a need for thermodynamic computing and a theory for it.  Current software systems maintain the integrity and internal structure of the objects and agents for free.  The computational energy is not visible to the software. 

Abbott admits that there are other definitions of agents which include schemata and evolutionary action, but he notes that the hurricane does not need these additional aspects.

Once agents support evolutionary mechanisms, these mechanisms can rapidly expand the complexity, M. Mitchell Waldrop describes a vision of complexity via:
  • Rich interactions that allow a system to undergo spontaneous self-organization
  • Systems that are adaptive
  • More predictability than chaotic systems by bringing order and chaos into
  • Balance at the edge of chaos 
of the system.  Abbott argues that this is an effect of the environment on the agent and vice versa, which he sees as just as important as the core physical forces, with the resulting expansion of
This web page reviews opportunities to find and capture new niches based on studying fitness landscapes using complex adaptive system (CAS) theory. 
Kauffman's adjacent possible
.  The emergence of platforms is noted as a major accelerator. 

Abbott concludes that science is reductionist, while nature is emergent with layers of emergent abstractions.  Physical and chemical entities are at equilibrium.  The hierarchy of properties supervenes and so the science seems coherent and hard.  In contrast the special sciences deal with autonomous entities which are difficult to bound and may not supervene.  These entities interact and perpetuate themselves through epiphenomena with dynamic emergence. 

Abbott argues that emergence has implications for software.  He views service based software as an autonomous entity extracting resources to perpetuate itself.  But he worries that the increasing leverage of shared services will increase the possibility of broad failures when a key node fails. 
Arbitrary base of epiphenomena and simulation
More generally Abbott concludes that emergence implies limits to the development of scientific theories.  The arbitrary base of any epiphenomenon will typically leave key aspects of the real implementation out of the theory.  As such any reasoning will fail to predict correctly interactions between the epiphenomenon and the hidden parts and that will limit the value of simulations.  Simulating the game of life was equally unlikely to identify gliders and Turing machines.  Instead human's use an evolutionary mechanism to detect and leverage these abstract design possibilities. 

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
depends directly on the
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergence
which Abbott explores.  Abbott associates autonomous agency with non-equilibrium actions.  We further consider it essential for components to be present which are able to bond into networked structures through which energy can
Flows of different kinds are essential to the operation of complex adaptive systems (CAS). 
Example flows are outlined.  Constraints on flows support the emergence of the systems.  Examples of constraints are discussed. 
flow, and that the flows can be constrained
.  Dynamic processes can then emerge.  Such component networks can support structure that can both represent information and induce focusing of the forces of underlying phenomena.  The
This page discusses the effect of the network on the agents participating in a complex adaptive system (CAS).  Small world and scale free networks are considered. 
network
's catalytic, an infrastructure amplifier.   action can additionally induce the replication of the catalytic structure allowing an
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. 
agent
to emerge. 

CAS
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
require the presence 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
and
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
.  The shared schemata, deployed through a
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
single cell developmental bottleneck
create a distributed system.  An evolving agent can benefit competitively from the chance implementation of a
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. 
Shewhart cycle
.  Once incorporated the CAS will have the property of intent, and can utilize design. 

In CAS systems the epiphenomena are
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. 
indirectly associated
through the schemata with the underlying phenomena from which they emerge. 
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Evolution
can thus act by operating on the schemata. 

We agree with Abbott that a thermodynamic aspect should be present in software system infrastructure.  CAS agents operating on Douglas Hofstadter and Melanie Mitchell's Copycat infrastructure do have to compete for access to the
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
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
provides a simple chemical environment, 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. 
, where, as agents deploy descriptors they affect both the basic forces instantiated by the Coderack, and the actions of the codelets which aggregate into agents

Abbott's paper reveals important aspects of the emergent properties of CAS systems.  





















































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