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\title{Behavioral Adaptive Autonomy. A milestone on the Alife route to AI?}
\author{Xabier Barandiaran$^{1}$ \\ \mbox{$^{1}$Department of Logic and Philosophy of Science, FICE, \\ UPV-EHU (University of the Basque Country) \\ PO BOX 1249 / 20080 Donostia - San Sebastian / Spain \\ barandi@sf.ehu.es \\ http://www.ehu.es/ias-research/barandiaran}}



\begin{document}
\maketitle

\begin{abstract}
While central to robotics, biology and cognitive science, the concept
of \emph{autonomy} remains still difficult to make operative in the realm of
Alife simulation models of cognitive agents. Its deep significance as a transition
concept between life and cognition (a milestone on the Alife route to AI) remains
obscured in the intricate relation between metabolic/constructive processes and behavioral adaptive processes in living systems.
Within a naturalized and biologically inspired dynamical approach to
cognition a definition of \emph{behavioral adaptive autonomy} is provided:
homeostatic maintenance of essential variables under viability
constraints through self-modulating behavioral coupling with the
environment, hierarchically decoupled from metabolic (constructive)
processes. This definition allows for a naturalized notion of
behavioral adaptive functionality (that defines a proper level of modelling within Alife), structurally and interactively emergent: the mapping of the agent-environment system's state space trajectories into the viability subspace of the essential variables of the organism.

\end{abstract}

\section{Introduction}


While central to Alife \citep{varelaandbourgine91, ruizmirazoandmoreno04}, robotics \citep{maes91}, biology \citep{varela79} and cognitive science \citep{christensenandhooker00}, the concept
of \emph{autonomy} remains still difficult to make operative in the realm of
Alife approaches to cognition\footnote{ We won't discuss here the notion of autonomy in relation to the origin and synthesis of life, this issue has long being discussed elsewhere \citep{ruizmirazoandmoreno04}. Our main concern is the notion of autonomy in the cognitive domain and its interaction with basic (autopoietic) autonomy.}. In particular it is not yet completely clear whether an artificial cognitive autonomous agent could be built without the underlaying autopoietic autonomy being implemented; and it is not clear how could the cognitive autonomy of an artificial or simulated agent be measured or implemented. The notion of autonomy and its deep significance remains obscured in the intricate relation between metabolic/constructive processes and behavioral/cognitive processes in living beings and in the highly abstract conceptual framework in which it has been developed by Francisco Varela.

But far from being a neglectable term, the notion of autonomy has inspired a whole range of research projects within the Alife community and it does in fact capture the core of the conceptual shift behind most Alife research (biological grounding, self-organization, emergence, embodiment and situatedness). In fact, the subtitle of the First European Conference on Artificial Life (``towards a practice of autonomous systems'') reflects the significance of the concept.

The main goal of this paper is to provide an specific definition of \emph{behavioral adaptive autonomy} that can be implemented in Alife scientific practices and used to model adaptive behavior. The main thesis is that the hierarchical decoupling of the nervous system from metabolic constraints specifies the domain of behavioral adaptive dynamics. In this domain autonomy is defined as the capacity of the system to interactively maintain its essential variables under viability constraints.

We start (section 2) by briefly analyzing the variety of uses that the term \emph{autonomy} has had in the literature and compiling (section 3) a set of key notions around autonomous approaches. Section 4 reconstructs the concept of basic (autopoietic) autonomy and the way it relates to functionality. We then move to specify the organization of the nervous system in the context of the whole organism (section 5) to end up defining autonomy and functionality in the dynamical framework of the behavioral adaptive domain (section 6). Finally section 7 discusses some implications of the present approach for Alife simulation models.


\section{A quick overview of the literature}

The term \emph{autonomous robotics} has been used since the 90s
\citep{maes91} to refer to a set of engineering constraints on the
construction and testing of robots, thus labeling a style of robotic
research in cognitive science and engineering. Such constraints
include conditions like no remote control of the agent, no external
energy supply, mobility in the robot, no human intervention in robot
task solving or real-time response in real-world environments. Close
to situated robotics \citep{brooks91a}, autonomous robotics
highlights physicality, embodiment, situatedness and dynamicism
versus abstract, virtual and formal approaches to artificial intelligence (in which agents operate in controlled formal
or virtual environments or in toy like worlds without dynamical
constraints). As a consequence of the real-world interaction of the robot the emphasis is often put
on the \emph{viability} of the robot as a task achieving agent: a
self-generated and robust capacity to respond to environmental
changes.

The practice of \emph{autonomous robotics} has forced some engineers
to go beyond the specification of a list of engineering constraints
and to develop a more elaborated notion of autonomy that specifies
the kind of interaction process that is established between the robot
and its physical environment, the dynamic structure and properties of
the control mechanisms and the underlaying consequences for
cognitive science and epistemology. That is the case of engineers
like Tim Smithers (\citeyear{smithers97}) or Randall Beer
(\citeyear{beer95, beer97}) who have strongly criticized computational information processing approaches to cognition highlighting dynamicism, embodiment and situatedness or Eric Prem (\citeyear{prem97}) who has put the emphasis on \emph{epistemic autonomy} ``the system's
own ability to decide upon the validity of measurements''
\citep{prem97} a process that cannot be reduced to formal aspects,
given the physicality of the measuring process, its pre-formal
nature.

These authors have been greatly influenced by the
biologist Francisco Varela whose definition of autonomy is much more abstract and
encompassing than its robotic application\footnote{ ``Autonomous systems are mechanistic (dynamic) systems defined as a unity by their organization. \emph{We shall say that
autonomous systems are organizationally closed. That is, their
organization is characterized by processes such that (1) the
processes are related as a network, so that they recursively depend
on each other in the generation and realization of the processes
themselves, and (2) they constitute the system as a unity
recognizable in the space (domain) in which the processes exist.}''
\citep[p.55, italics in the original]{varela79}.}. Autonomy is defined by Varela as an abstract systemic kind of organization; a kind of
self-maintained, self-reinforced and self-regulated system dynamics
resulting from a highly recursive network of processes that generates
and maintains internal invariants in the face of internal and
external perturbations. A process that defines its own identity; i.e.
its unity as a system distinguishable from the surrounding processes. This abstract notion
of autonomy is realized at different biological scales and domains. It is precisely the autonomy of each domain what defines its specificity. As a paradigmatic example ``life'' is defined as a
special kind of autonomy: \emph{autopoiesis} or autonomy in the
physical space. In turn ``adaptive and cognitive behavior'' is the result of a
higher level of autonomy: that of the nervous system, producing
invariant patterns of sensorimotor correlations and defining the
behaving organism as a mobile unit in space \citep{varela79,  varelaandbourgine91, varela91}.

But Varela's perspective on autonomy (although highly influential)
has been recently criticized by its emphasis on closure\footnote{ It is
precisely this emphasis on operational closure and its algebraic definition in \cite{varela79} what makes controversial to apply Varela's notion of autonomy to a dynamical modelling of biological systems. Nonetheless Varela addresses several times the issue of a dynamical modelling of autonomy (pages 56, 86, 201 and 264) and concludes: ``(...) I see these tools [dynamical system theory and computer simulations] as one way in which properties of systems, autonomous or allonomous, can be expressed. Differentiable dynamics represent, in practice, the most workable framework in which these two points of view can actually coexist and be seen as complementary in an effective way.'' \cite[p.164]{varela79}} and the
secondary role that system-environment interactions play in the
definition and constitution of autonomous systems. Introducing
ideas from complexity theory and thermodynamics authors like Bickhard
(\citeyear{bickhard00}), Christensen and Hooker
(\citeyear{christensenandhooker00}), Collier (\citeyear{collier02}), and Ruiz-Mirazo and Moreno
(\citeyear{ruizmirazoandmoreno04}), have defended a more specific
notion of autonomy as a recursively self-maintaining
far-from-equilibrium and thermodynamically open system. The
interactive side of autonomy is essential in the definition:
autonomous systems must interact continuously to assure the necessary
flow of matter and energy for their self-maintenance. The
philosophical consequences derived from the nature of autonomous
systems are highlighted by these authors and summarized by Collier in the slogan:
``No meaning without intention; no intention without function;
no function without autonomy.'' \citep{collier02}. Autonomy is made
the naturalized basis for functionality, intentionality, meaning and
normativity.
But it is not always clear what the relation is between this basic thermodynamic or constructive autonomy and neurally guided adaptive behavior. It is even
argued that dynamical system theory (and thus computational
simulation models in Alife) cannot capture the kind of organization that
autonomy is \citep{christensenandhooker00} or that robots should be self-constructive in order to be ``truly'' autonomous \citep{ruizmirazoandmoreno00}.


\section{Key notions covered by autonomous approaches to cognition}

In general and across the differences between the uses of the term
autonomy and the consequences that (more or less explicitly) are
derived from it, the notion of autonomy subsumes a set of key notions
in cognitive science that have been pushing towards a paradigmatic
shift. Among this key notions we find:

\begin{itemize}
\item \textbf{Biological grounding}: the idea that the understanding of
cognition and adaptive behavior must be approached bottom-up at two
levels: in terms of the evolution of cognitive capacities in natural history and in
terms of the biological mechanisms (neural networks, bodies,
neuroendocrine systems, etc.) that produce cognitive behavior.

\item \textbf{Self-organization, complexity, emergence}: the idea that there is
no central processor or homunculi that controls behavior but a
distributed and functionally integrated network of recursive
processes from which a coherent behavior emerges as a global product
of the system. The notion of autonomy assumes a high degree of
complexity in the system introducing constraints on the possible analysis
and functional localization and decomposition of structures.

\item \textbf{Interactivism, embodiment, situatedness, dynamicism}: Cognition
is a process whose development and realization cannot be decoupled
from the embodied interaction processes in which it is situated. An
autonomous approach assumes a dialectics between independence and
structural coupling: an interactive construction of meaning and
behavior in which embodiment and situatedness are taken to be
essential features of cognition that are best captured by dynamical
(rather than traditional computational) notions, thus introducing
time and space dependant constraints as essential features on the
generation of behavior.

\item \textbf{Critics to GOFAI}: The use of the notion of autonomy is often
associated with a profound critique to what has been the mainstrain
paradigm in cognitive science: the view that cognitive processes are
logical transformations of computational states bearing a
representational relation with observer independent ``states of
affairs'' in the world. A view where the representational relation is
taken to be the mark of the mental and the program-like
transformation rules between representational states the causally
effective mechanisms in the production of behavior. From autonomous
robotics to the philosophy of biology and cognition, the approaches
focused on autonomy have taken a different starting point, different
theoretical primitives from which theories of cognition and
adaptation have been built (complex dynamic networks, physically
and thermodynamically embodied interactions, decentralized control
systems, biologically grounded subsymbolic processes) to specifically address some of the problems that GOFAI approaches suffered at both practical-engineering and theoretical-philosophical levels\footnote{ In this sense
autonomy refers to explanations and design principles grounded on the internally driven interactive organization of the system; and not on representational or causally correlated relations between agent and environment (and often heteronomously interpreted or designed by and external observer-engineer).}.
\end{description}

So far so good, the concept of autonomy subsumes a set of new approaches to cognitive science... But what else? Is \emph{autonomy} just an umbrella label to cover an undetermined set of general constraints in robotic and cognitive science? Is it just a heavy-weighted
metaphysical concept that only makes sense under the conceptual
framework developed by Maturana and Varela? Or can it be conceptually
and methodologically tunned in order to be introduced as a
scientifically productive concept in empirical and synthetic research? The remaining of the paper will try provide an explicit and positive answer to this question.


\section{Basic autonomy: the root for normative functionality}
\label{aba}

The origin of the word autonomy comes from the Greek
\emph{auto-nomos} (self-law). We can thus provide an intuitive first
notion of autonomous systems as those producing their own
laws\footnote{ Strictly speaking
new physical \emph{laws} will never be created by an organism (or
any other macroscopic system) but constraints can be generated that
specify and govern its behavior.}. But this notion
requires a previous notion of self: autonomous systems must first
produce their own identity; i.e. autonomous systems are primarily
those whose basic organization is that of a self-sustaining,
self-constructing entity over time and space. Their being is a process of recursive production of their constituting structure: a recursivity that generates a self. It is on top of this sense of
\emph{basic autonomy} that other levels of autonomy will appear in natural
systems.


\subsection{Basic Autonomy}

Basic autonomy \citep{ruizmirazoandmoreno00} is the organization by
which far from equilibrium and thermodynamically open systems
adaptively generate internal and interactive constraints to modulate
the flow of matter and energy required for their self-maintenance.
Two equally fundamental but distinct aspects of basic autonomy can be
distinguished:

\begin{description}
\item[a) \emph{constructive}:] generation of \emph{internal}
constraints to control the internal flow of matter and energy for
self-maintenance. In this sense the autonomous (autopoietic) system can be
understood as a highly recursive network of processes that produces
the components that constitute the network itself
\citep{maturanaandvarela80}. Metabolism is the expression of
this constructive aspect.

\item[b) \emph{interactive}:] the generation of
\emph{interactive} constraints modulating the boundary conditions
of the system to assure the necessary flow of energy and matter
between the system and its environment\footnote{ Unlike dissipative structures which hold their
organization only under a restricted set of external conditions that
the system cannot modify.}. Active transport through the membrane of a cell, control of
behavior or breathing are characteristic examples of this
interactive constraint generation.
\end{description}

On this basis we can define \emph{constructive closure} as the
satisfaction of constructive constraint generation and
\emph{interactive closure} as the satisfaction of interactive
constraint generation for self maintenance.

In general autonomy, at any level, will always present a twofold
dialectics between internal recursive process and the necessary
interactions to maintain them. In autonomous
systems internal dynamics are more cohesive and integrated (more complex) than the
interactive dynamics it sustains, thus producing a dynamic control asymmetry  laden to the side of
the autonomous agent.


\subsection{The origin of functionality and normativity}

What defines functionality in autonomous systems is the satisfaction of closure conditions \citep{collier02} of internal and
interactive processes. A process (internal or interactive)
is functional if it contributes to the global self-maintenance of the system.

In turn functions become \emph{normative}\footnote{
Normativity refers to the value attribution that is given to a
process or object; e.g. adaptive or maladaptive to an interaction
or structure in an organism, true or false to a cognitive state or
believe, beautiful or ugly to a work of art, etc. Normativity
challenges physicalist scientific approaches to the understanding of
our world because it introduces a value asymmetry (good/bad,
true/false, adapted/maladapted) in the description of nature, an
asymmetry that is not present in any of the fundamental laws of
physics. But, although alien to fundamental physics, \emph{normativity}
is an essential component of biology and cognitive science (and consequently for Alife and AI): whether an structure or interaction is
adaptive or maladaptive for an organism is a value judgment that a
scientist engaged in the study of living and intelligent systems must do. A
judgment that must be justified in naturalistic terms; i.e. in the very organization of the
system under study and not from a set of value preferences in the observer scientist.} by means of the \emph{dynamic
presupposition} of that process in the overall organization of the
system \citep{christensenandbickhard02} since constructive and interactive functional processes are \emph{the condition of possibility} of autonomous systems (as far
from equilibrium and recursively self-maintained systems).  The strength of an autonomous perspective resides in the fact that it is the very system who
determines and specifies it. It is not an external observer who attributes
functions to structures imposing a normative criteria according
to its correspondence with states of affairs in the world. Nor is it
on the basis of the agents evolutionary history \cite{millikan89b} or its structural matching with the environment that processes or structures acquire a function.


\section{The organization of the nervous system}

Following \cite{morenoandlasa03} if an autonomous system needs
to recruit the same infrastructure to
achieve both constructive and interactive closure then the
space of possible biological organization becomes highly
constrained. This happens because metabolic reactions (constructive
processes) are slower than the reaction times required for available
interactive closure opportunities, specially those available for
fast body movements (motility) in big organisms (where the
relative difference in velocity between metabolic reactions and body
movement increases). Thus if a subset of the interactive closure is
achieved and controlled by a structure that instantiates processes
which are dynamically decoupled from the constructive ones, the space
of viable system organization is expanded. That's precisely the
origin of the nervous system: the new opportunities for survival
offered by the hierarchical decoupling of the nervous system, i.e.
behavioral control decoupled from metabolic (constructive)
constraints. The relation between metabolic constructive processes (M) and the nervous
system (NS) is characterized by four properties that
specify the organization\footnote{ The identity characterizing properties of a system, i.e. the set of properties that identify a system as being a member of class.} of the nervous system in the context of
the whole organism:

\begin{enumerate}
\item\textbf{Hierarchical decoupling of the NS from M:} The NS is
hierarchically decoupled from M by the:
\begin{enumerate}
\item\textbf{Bottom-up, local, constructive causation of the NS by M:}
constructive/metabolic processes produce and maintain the architecture
of the nervous system (neural cells, synapses, myelin, etc.) thus sustaining a new dynamical domain, new
variables and relations between variables: the NS. The constructive
nature of this causation establishes the \emph{hierarchical} aspect
of the decoupling.
\item\textbf{Dynamic underdetermination of NS by M:} the dynamic
state of the NS is underdetermined by metabolic dynamics, i.e. neural dynamics are
enabled but not
determined by the metabolic production of the neural architecture.
This underdetermination specifies the \emph{decoupling} side of
the relation.
\end{enumerate}
\item\textbf{Downward causal dependency of M on NS:}
Because the NS performs interactive functionality for the
self-maintenance of the system, M depends on the proper functioning
of NS; i.e. the organism's survival depends on neurally controlled behavior.
\item\textbf{Global and dynamic meta-regulation of NS by M:}
Although the NS is dynamically underdetermined by M, M
establishes the metaestability condition for the NS because the NS's
functionality is defined by its interactive contribution to
self-maintenance (and this must ultimately be evaluated by M). M does not
directly evaluate the NS's dynamics but the interactive closure: i.e. the
input of matter and energy it gets from the environment. But this
meta-regulation, again, underdetermines the dynamics of the NS.
Metabolism only indicates if a particular coupling is successful or
not in the satisfaction of interactive closure conditions, but does not
determine which one of all the possible viable/adaptive couplings should the NS
undergo.
\item\textbf{Internal cohesive dynamics of the NS:} \label{cohesivedynamics} The
other side of the metabolic constructive and meta-regulatory
underdetermination of the NS's dynamic state is the recursive capacity of the NS
to maintain invariant patterns under internal and external
perturbations; i.e. its capacity for self-generated cohesion, the degree
in which the system's internal dynamics are more complex than the
interactive flow so that the former can control the later to compensate for internal and external perturbations\footnote{ This is close to what Varela refers to as ``operational closure'' although we believe that internal cohesion is achieved through interaction processes rather than through internal recursivity alone: i.e. closed sensorimotor loops are integrated in the recursive functioning of neural dynamics.}.
\end{enumerate}


We can now abstract a second domain in biological systems
(hierarchically decoupled from basic autonomy): \emph{the domain of
the organism's behavioral adaptive dynamics}, specified by the organization of the nervous system. This new dynamic domain, decoupled from local metabolic processes,
provides a qualitative lower level (epistemological) boundary for the characterization
of the specificity of cognition and allows for specific dynamical
modelling of adaptive behavior. It is in this modelling domain that we will
be able to define behavioral adaptive autonomy and thus a new
level of functionality (properly cognitive but still biologically grounded).



\section{Dynamical modelling of autonomy and functionality in the behavioral domain}

% CAMBIAR EL NOMBRE DE ESSENTIAL VARIABLES A OTRA COSA.

Dynamically considered metabolism only acts as a set of control
parameters for the nervous system; the behavioral domain is
dynamically blind to metabolism's constructive functioning (although it has to be sensible to global metabolic conditions). Thus
the constructive processes of basic autonomy can be modelled as a set
of essential variables which tend to stay away from equilibrium;
representing the cohesive limits of constructive processes and
their interactive closure conditions. A similar approach was already
taken by \cite{ashby52} half a century ago (from whom we have taken
the term essential variables) and recently recovered by \cite{beer97}
and \cite{dipaolo03} in (evolutionary) simulation modelling of
adaptive behavior. The dynamical autonomy of the behavioral domain
allows for a naturalistically justified assumption of dynamical
system theory (DST) as the proper conceptual framework to think about
autonomy and cognition in this domain. If we model:
a) the agent's NS and the
environment as coupled dynamical systems (situatedness),
b) coupled through sensory and motor transfer functions
(embodiment), and
c) the metabolic processes as essential (far from
equilibrium) variables only controllable from the environment and
signalling the NS;
we get that functionality and autonomy can be redefined in the
behavioral domain.





\subsection{Behavioral adaptive autonomy}


In the behavioral domain thus considered, a new level of autonomy
can be described, hierarchically decoupled but interlocked with basic (metabolic)
autonomy: \emph{behavioral adaptive autonomy}.

We can now, in dynamical terms, explicitly define \emph{behavioral
adaptive autonomy} as:

\begin{quote}
  homeostatic maintenance of essential variables under
  viability constraints [\textbf{adaptivity}] through a self-modulating
  behavioral coupling with the environment [\textbf{agency}],
  hierarchically decoupled from metabolic (constructive) processes
  [\textbf{domain specificity}].
\end{quote}

This definition highlights three main aspects of behavioral adaptive
autonomy:

\textbf{Adaptivity:} The ``homeostatic maintenance of essential
variables under viability constraints'' condition assures a naturalized and autonomous criteria for (adaptive) functionality.
Adaptivity is thus defined from the perspective of the maintenance of the
organism, not from the perspective of structural adequation between the organism and the environment. It is not the organism that matches the environment in a given prespecified way. On the contrary it is through the particular way in which the agent satisfies the homeostatic maintenance of essential variables that an adaptive environment (a world) is specified cut out from a background of unspecific physical surroundings. Next section will further analyze the the nature of behavioral adaptive functionality thus considered.

\textbf{Agency:} The ``self-modulating behavioral coupling'' condition for behavioral adaptive autonomy specifies the \emph{agency} of the organism in the adaptive process. ``Self-modulation'' is the consequence of the cohesive dynamics of the nervous system by which its dynamics are more complex than the interactive ones in the generation of the internal invariants (the homeostatic maintenance of essential variables under viability constraints). The notion of self-modulation refers to this control asymmetry in the production of behavior and that's precisely what we call agency. It can't be otherwise, if the state of essential
  variables is only accessible for the agent (through internal
  sensors: level of glucose, feeling of hot, pain, etc.) the
  homeostatic regulation must be guided by the agent's nervous system
  and not by the environment. Thus the NS needs to evaluate it's
  structural coupling through value signals from the essential
  variables. This way a \emph{value system} guided by the state of
  essential variables and acting as metaestability condition for
  structural plasticity of sensorimotor transformations becomes a
  fundamental component of behavioral autonomy, and a defining
  component of agency. The higher the
  agent's capacity for adaptively guided self re-structuring
  (plasticity) the higher it's behavioral adaptive autonomy and
  hence its agency\footnote{ By this condition external contributions to adaptation (such as parents care or artificially induced constraints in toy-like worlds), although functional for the agent, would be excluded from the domain of autonomous adaptation.}.

\textbf{Domain specificity:} The hierarchical decoupling of the
  nervous system from metabolic processes provides a naturalized
  criteria for the domain specificity of behavioral autonomy,
  distinct from other adaptive domains in nature (bacterian
  networks, plants, etc.). This domain specificity should not
  be considered as independency but as hierarchical decoupling
  (explained above), which allows for a justified specific modelling
  of behavioral autonomy separated from local constructive
  aspects. Nonetheless it should be noted that two kinds of autonomy are interlocked
  here: basic autonomy and behavioral autonomy. Both domains are
  mutually required, the behavioral domain satisfies interactive
  closure of basic autonomy and basic autonomy constructs the bodily
  and neural variables defining the NS's architecture. At the same time basic autonomy acts as a meta-regulator of the NS's dynamics.




\subsection{Behavioral adaptive functionality}

Functionality, in the behavioral domain thus considered, can be
defined as the homeostatic effect of an interaction process on the maintenance of essential variables under viability constraints and, more specifically, as the mapping of the interactive trajectories (in the agent-environment coupled dynamic space) into the state space of the essential variables. Normativity is transitive from basic autonomy to
the behavioral domain through the maintenance of essential variables
under viability constraints. Thus normative functionality
(adaptivity) is \emph{the mapping of the agent-environment coupled system's state space trajectories into the viability subspace of the essential variables}.

Because this definition of function does not compromise
any structural decomposition in functional primitives (unlike
traditional functionalism), a dynamical approach to behavioral
functionality can hold two kinds of emergence\footnote{ We are here
talking of weak emergence in the sense of an holistic, recursive and
distributed causal structure that produces a global
ordered/invariant pattern. We are not arguing for a strong or
ontological emergence that defends the appearance of a new
property or object non reducible to the underlying processes.}:

% no me gusta el término structural emergence porque la estructura es
% pasiva
\begin{description}
\item[a) \emph{Internal emergence}:]{It appears when the agent's
internal structure is causally integrated (and the NS often is), i.e.
interactions between components are non-linear and components are
highly inter-connected. Functional decomposition of components
(localization) is not possible. The functionality of the system
\emph{emerges} from local non-linear and recursive interactions between
components.}
% faltaría leer la presentación de Bechtel sobre emergencia en
% ishcabibel
\item[b) \emph{Interactive emergence}:]{Because
essential variables are non-controlled variables for the agent,
functionality is interactively emergent \citep{steels91,
hendriks-jansen96}, not in the trivial sense that essential variables
need external input, but in the sense that achieving this often
requires closed sensorimotor loops for the agent to enact the
necessary sensorimotor invariants to control essential
variables.\footnote{ Very often interactive emergence reinforces
internal emergence because ``interactions between separate
sub-systems are not limited to directly visible connecting links
between them, but also include interactions mediated via the
environment'' \citep[p.205]{harveyetal97}}} \end{description}

What this double emergent condition shows is that the way the
specific adaptive function is achieved involves a dynamic
coupling between agent and environment where no particular
decomposable structure of the agent can be mapped into functional
components: functionality is the outcome of an interaction
\emph{process} (that can be modified by the cognitive agent according to its perceived satisfaction of closure conditions).


\section{Discussion}

Now, the problem with behavioral adaptive autonomy is
the problem of a higher characterization and development of its
understanding, specially in relation to its self-regulating,
emergent and complex nature which does not allow for a
localizationist program to succeed: i.e. functional and structural
decomposition of components and aggregative causal abstraction of mutual relations
\citep{bechtelandrichardson93}. When localizationist strategies are thrown away the locus of
the research enquiry regarding the nature and origin of cognition and adaptation
is displaced towards: a) the specification of the dynamic structure of lower
level mechanisms capable of implementing behavioral adaptive
autonomy (i.e. capable of self-restructuring cohesive and recursive dynamics); and
b) the search for the nature of intermediate explanatory patterns
between the agent-environment structural coupling and the maintenance
of essential variables under viability constraints: traditional explanatory
concepts (such as information, representation, memory, processing, etc.)
should be dynamically grounded if introduced at all in the proposed framework. In this sense the view on \emph{behavioral adaptive autonomy} presented here is closer to highly integrated and functionally unespecific models (such as those of evolutionary robotics) than action selection modelling techniques \citep{humphrys96}, where possible actions are pre-specified and the agents internal structure is unable to reconceptualize an interactive domain to achieve novel functionality. Behavioral adaptive autonomy is neither something to be achieved just by introducing energetic constraints on robot task solving \citep{kellyetal99}. 

A-life and, more specifically, evolutionary simulation modelling\footnote{ Evolutionary robotics
\citep{harveyetal97, nolfiandfloreano00} and Randall Beer's
minimally cognitive behavior program \citep{beer01} being the major exponents here.} becomes a
mayor research tool here through the synthesis and experimental manipulation and analysis of the behavior generated by embodied and situated DRNNs (dynamic recurrent neural networks).
The simulation model acts as an artifactual blending between lower level neural mechanistic
concepts and the global functional conceptualization of behavior
\citep{barandiaranandfeltrero03}.



An interesting line of research has recently been proposed by
\cite{dipaolo03} in this direction. Di Paolo argues that behavior
itself is underdetermined by survival conditions and proposes
\emph{habit formation} as the origin of intentionality. Habits are
self sustaining dynamic structures of behavioral patterns,
sensorimotor invariants homeostatically maintained by neural
organization. Homeostatically controlled synaptic plasticity
\citep{turrigiano99} could be a relevant neural organization leading
to such autonomy of behavioral patterns; as demonstrated by
\cite{dipaolo00}. Although habit formation does not necesarily address the issue of the relation between metabolic and nervous autonomy Di Paolo points towards a fundamental step forward in current research trends: that structural coupling (and closed sensorimotor loops) is not all there is in a dynamical and situated approach and that a robust capacity of the agent to evaluate and restructure its coupling is the way to follow to achieve progresively higher levels of cognitive autonomy and intentionality.

If Alife is to throw some light on the origin of cognition and adaptive behavior, far from equilibrium essential variables and value systems capable of specifying stability conditions for a given dynamical coupling with the environment should be introduced in the simulation models. In particular essential variable based fitness functions in evolutionary simulation modelling are a particular instantiation of behavioral, internal and implicit fitness functions which (according to Floreano and Urlezai ---\citeyear{floreanoandurzelai00}) shall produce highly self-organized control systems. This principles for evolutionary simulation modelling of autonomous agents were succesfully implemented in a foraging task with alternate profitability sources \citep{barandiaran02}.

In addition to this synthetic bottom-up methodology other
analytic tools should be theoretically tunned. Complexity measures
to understand functional integration in neural processes
\citep{tononietal98} are producing interesting results, an could be used to better characterize the cohesive nature of the NS. An early
exploratory example of such methodology is provided by
\cite{seth02}, fusioning both evolutionary simulation modelling
and complexity measures of neural network dynamics (using dynamical graph theory) to analyze the relation between behavioral (interactive) and neural (internal) complexity.



\section{Conclusion}

A wide use of the term autonomy is found in the Alife literature: from a set of engineering constraints in robotics to a fundamental organizational principle in biology. In relation to behavior and cognition it is not clear how to operationalize the term and what the relation is between behavioral autonomy and basic (autopoietic) autonomy. We have seen that the particular organization of the nervous system allows for a specific modelling domain of cognition and adaptive behavior: the domain of behavioral adaptive dynamics. Interlocked with basic/metabolic autonomy (through the requirement to actively maintain essential variables under viability constraints) \emph{behavioral adaptive autonomy} becomes a process of cohesive maintenance of internal invariants through continuous interaction loops, which requires, in turn, a functionally integrated and plastic neural organization with a higher internal dynamic complexity than that established between the organism and its environment.

By providing an explicit definition of behavioral autonomy and functionality in dynamical terms we hope to have contributed something to the simulation modelling approach that traces the Alife route to AI; to the understanding of the transition that goes from basic forms of life to adaptive behavior and cognition.

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\section*{Acknowledgments}

I want to acknowledge the Basque Government for financial support through the doctoral fellowship BFI03371-AE and the University of the Basque Country for the research project grant 9/UPV 00003.230-13707/2001. I want to thank Alvaro Moreno and Ezequiel Di Paolo for usefull comments and discussion on the topics presented here. Thanks also to Jesus Siqueiros and Tomas Garcia for corrections on the final manuscript.

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