The Emergence of Interactive Meaning Processes in Autonomous Systems Argyris Arnellos, Thomas Spyrou, John Darzentas University of the Aegean Dept of Product & Systems Design Eng Syros, Greece

General Problem Theoretical frameworks of cognition are differentiated by the way they handle the notions of intentionality, meaning, representation and information. One could ask: How is meaning generated and manipulated in natural and consequently in artificial cognitive systems?

MAIN APPROACHES to GOGNITION and to the DESIGN of ARTIFICIAL AGENTS Cognitivism and Computational Artificial Agents All intentional content is a kind of information which is externally transmitted by a merely causal flow. Meaning is externally ascribed  An Objection: Searle’s Chinese Room Argument  An Answer: (Harnad, 1990): Symbol Grounding is an Empirical Issue Note: A complete cognitivist grounding theory should consider both external and internal representational content, as well as, their transduction system and its interactive nature. Main candidate for the transduction is a connectionist network

Connectionism Connectionist systems as syntactically adaptive systems (not all of them) Such machines receive (contingent) feedback from their outputs, which then directs the adjustment of their decision function (i.e.new percept-action mapping). –No semantics as the system cannot decide on its own which aspects of the world must be encoded ("feature primitives") such that the machine can find a successful classification rule. –Adding a "training" origin to correspondence works no better than adding lawfulness –Connectionistic architectures cannot account for internal content.

Dynamic Systems and Cognition Time-Dependence: Natural cognition happens in real time, hence dynamics is better suited to model it than the a-temporal computational approach. Embodiment: Cognition is embedded in a nervous system, in a body, and in an environment, whereas computationalism typically abstracts this embeddedness away, and can incorporate it in only an ad hoc manner. Emergence: Dynamics can explain the emergence and stability of cognition through self-organisation, whereas cognitivism ignores the problem of cognitive emergence.

Dynamic Systems and Cognition No information processing (no symbols, no representations) The dynamics of the cognitive substrate (matter) are taken to be the only thing responsible for its self- organization System’s ability for classification is dependent on the richness of its attractors, which are used to represent events in its environment System’s meaning evolving threshold cannot transcend its attractor’s landscape complexity

Defining Agency Strong notion: An agent is a system which exhibits: interactivity: the ability to perceive and act upon its environment by taking the initiative; intentionality: the ability to effect goal-oriented interaction by attributing purposes, beliefs and desires to its actions; autonomy: the ability to operate intentionally and interactively based only on its own resources. [Collier, 1999] suggests that there is: no function without autonomy; no intentionality without function; no meaning without intentionality; Circle closes by considering meaning as a prerequisite for the maintenance of system’s autonomy during its interaction.

The Need for a New Kind of Representations “As tasks become more complex the use of internal states that carry information about the environment becomes less and less avoidable.” (Kirsh, 1991) “…even in the very simple cases mentioned above we find that individual units act as very simple representations in mediating interactions between the robot and its world” Brooks (1997) What kind of representations do we need? No representations per se, but a different type of representation

The Need for a New Kind of Representations Representations that can only be understood in the context of activity. For an adaptive system the primary problem is to produce action appropriate to the context, not referentially individuate a signal source (cognitivism). The content should be accessible to the system itself.

Functionality and Representations A behaviour is ‘really’ contributing to systems functionality if and only if it is mediated by representations and an information-carrier is only a representation if it plays an appropriate role in the systems functionality towards its self- maintenance Where can these kind of representations be found and What type will they be?

Code Duality in protein structure/sequences based on (Hoffmeyer & Emmeche, 1991) Analog Information Space: protein functional conformations Digital Information Space: amino-acid sequences AIS H  K DIS K sequence:structure

Levels of Interactive Representations (Bickhard, 1998) Interactivism and Function Function is a forward looking concept as it tries to explain what is its future value to the system. Recursively Self-Maintenant Systems System has alternative ways of self-maintenance available and it can switch one alternative to another in case of failure. The conditions under which the serving of a function succeeds constitute the dynamic presuppositions of those functional processes. a minimal ontological representative system (S) has to include a subsystem, a differentiator (Dif), engaging in interaction with its environment (Env).

Levels of Interactive Representations (Bickhard, 1998) Internal course of that interaction will depend both on the organization of the subsystem and on the interactive properties of the environment. Each final state classifies all of the environments together that would yield that particular final state if interacted with. Each possible final state (FS) will serve as a differentiation of its class of environments Env1 Env2 Env3 System Dif FS1 FS2 Goal System 1 Goal System 2 PiPi PjPj

Emergent Levels of Interactive Representations Level 3: Implicit definitions of environmental categories Final states reached can be considered as a digitalisation of the analog- analog interactions in the internal of the system due to its contact with the environment. Minimal information: There is no information concerning anything about that environment beyond the fact that it was just encountered and that it is not the same as those environments differentiated by any of the other possible final states. There is no representational content involved System has no information about the classes of environments that it implicitly defines.

Emergent Levels of Interactive Representations Level 4: Functional Interactive Predication System strives to achieve maintain self-maintenance and through its interactions builds a new level of organisation (new representational level) where the implicit environmental differentiations of Level3 are re-organised as quantitative variety of functional predications about the environment. From Level3  Level 4 - Minimal representation: The whole system at this moment (FI) interprets the signs of Level3 as Dynamic Interpretants at Level4 Differentiator’s final states (FS) (Representamens) indicate which further procedures might be appropriate and the goal- system selects from among them. Analog-driven Emergence where new predicates are formed

Levels of Interactive Representations Env1 Env2 Env3 System Dif FS1 FS2 Goal System 1 Goal System 2 PiPi PjPj System’s Habit Representamen Dynamic Interpretant

Levels of Interactive Representations Level 5: Implicit definitions of environmental properties Interaction with the environment continuous and the AIS of Level4 are locally interacting in various time scales in order to reduce uncertainty for the environment. A in a way more compressed digital record emerges and we have a transition from implicit definitions of environmental categories  implicit definitions of environmental interactive properties From Level4  Level 5: Emergence of functional relations among system’s organisations that involve such implicit definitions:  Implicitness and presupposition is observed which can account for unbounded representationality.

Levels of Interactive Representations Level 6: Emergence of organisations of interactive potentialities Level’s 5 representations are implicitly being selected by system’s differentiating interactions in a “statistical manner”  formation of aggregates of properties that are presently available. –These aggregates are ongoingly updates  construction of new indications and changing old ones  formation of apperceptive procedures. From Explicit Situation Images  Implicit Situation Images Implicit definitions of environmental interactive properties – Level5) ENGAGE in various apperceptive procedures driven by Level4 and forms organisations of interactive potentialities

Levels of Interactive Representations Level 7: Emergence of objects and Constructive Memory The organisations of indications of interactive potentialities are used in system’s interaction and in some cases they tend to remain constant (invariant) as patterns.  The quantitative variety of the organisations of interactive potentialities of Level 6 is re-organised as certain types of organisations (based on their temporal coherence).  Such types of organisations of interactive potentialities constitute objects for the system itself. Memory: System is able to expand its situation image without explicit bound  system represents such invariances in its situation image. Constructive Memory System is able to test past apperceptive processes in present and differing directions.

Levels of Interactive Representations Level 7: Emergence of objects and Constructive Memory At this phase, icons and indexes can emerge into the system but not symbols, for which genuine social communication is needed. Symbols will need the lower levels Notes: A useful framework for Alife and AI experiments (since, interactive representations need only simple control systems) It seems that initially two non-semiotic levels should exist?

IOII FI sos of representational structure DI pragmatics system’s history Testing anticipations object II FI Morphodynamics action COGNITIVE SYSTEM ENVIRONMENT DO Memory-based analogy making measurement PS Rule-based syntactic complexity DO signs Abduction Deduction Induction Full Semiotic and Representational Capacity