Modeling Interactive Intelligences

Luis O. Arata

Department of Fine Arts, Languages, and Philosophy

Quinnipiac University

Hamden, CT 06518

ABSTRACT

This paper explores modeling the adaptive mechanisms of

autonomous agents. The focus is on reflexive interaction as a

looping action that creates a “self.”

 Imitation and play are adaptive components of reflexive

interactions that can provide gradual modifications of the

agent’s self. Little by little, the autonomous agent reinforces

what seems to work and phases out other options through an

internal editing process.

 A performance metrics for reflexive interaction would

have to depend on the task at hand within a context, rather

than try to be an absolute measure. Evaluation of

performance has to be flexible enough to account for multiple

intelligences especially when innovation is possible.

KEYWORDS: reflexive interaction, open loops,

modeling, play.

INTRODUCTION

This paper explores modeling the adaptive

mechanisms of autonomous agents. The focus is

on reflexive interaction, which is the making of

open loops that combine feedback and

feedforward capabilities to interact dynamically

with the environment.

 Feedback and feedforward can be seen as

reaction and proaction, as sensing and probing.

An agent that can loop effectively the work of its

sensors, probes, and tools would then have the

capacity to interact reflexively with its

environment. The agent’s mechanism of reflexive

interaction constitutes a "self."

 The agent’s looping action creates internal

maps to translate interactions and compare them

continuously to previous ones stored in its

memory in order to adapt to changes. Imitation

and play are components of reflexive interactions

that can provide gradual modifications to the

internal maps of the self in adaptive systems. The

mapping process itself can be compared to the

writing and editing of a text, where grammar,

data, and ideas interact to form an effective map.

Little by little, the autonomous agent reinforces

what seems to work and phases out other

options through an internal editing process.

 To explore reflexive interaction, I begin with

a presentation of how Rodolfo Llinás describes

mapping, and Gerald Edelman’s related concepts

of reentry and binding. Next, I rely on Jean

Piaget’s model of adaptation in order to examine

the function of imitation and play in an

autonomous agent. These concepts come

together in the model of the self that Rodolfo

Llinás developed as a device that situates the

agent in an environment and helps it navigate

safely. Finally, I look at mobility of a reflexive

agent fueled by two factors: an external changing

environment and internal changes that motivate

the agent to drift into what Stuart Kauffman calls

the adjacent possible.

 A performance metrics for reflexive

interaction would have to depend on the task at

hand within a context, rather than try to be an

absolute measure. Evaluation of performance has

to be flexible enough to account for multiple

intelligences. It should not limit the freedom to

develop diverse approaches in the making of

agents as well as in the way agents carry out

tasks especially when innovation is possible.
1. REFLEXIVE INTERACTIONS

When an autonomous agent performs tasks in an

environment, change can happen to both the

environment and the agent. This process of

mutual change is an interaction [1]. Interaction,

rather than action upon objects, complicates the

dynamics of a task but it does help model more

closely how events happen in the real world, so

to speak.

 Next, we note that interactions form a link

that can create a circulation or a loop between

the linked parts. This is reflexivity. The

performance of an autonomous agent is affected

by its ability to reflect as it interacts.

 Two questions then come up. How does

reflexivity work to enhance the agent’s ability to

perform a task? And how could this reflexivity be

gauged so that it may be fine tuned with respect

to tasks?

 In I of the Vortex, Rodolfo Llinás develops

a fascinating model of the self, based on

interactive feedback and feedforward loops. He

begins with a view of the brain as a system that

does isomorphic sensory-motor transformations

of the outside world. This creates representations

that help the body act in the outside world.

Llinás then follows the lessons of the sea

squirt. This tiny sea creature has mobile state

followed by a plant-like one. During the first

phase, it has a brain. But when it finally attaches

itself to a surface, the sea squirt digests its own

brain along with the tail that provided motility.

Llinás concludes that “the evolutionary

development of a nervous system is an exclusive

property of actively moving creatures” [2]. The

nervous system and particularly the brain are

predictive instruments that allow the organism to

move more safely in search of food, often in a

potentially hostile environment. The brain creates

working models of the environment to give the

body interactive navigational capabilities. Llinás

imagines that such models are very much dreams

of our brain, and in the waking state those

dreams are guided and shaped by the senses:

“the fact is that we are basically dreaming

machines that construct virtual models of the real

world.” In effect, what we perceive is a virtual

world.

 The sense of self emerges from interactions in

the brain as it coordinates actions. The self is an

avatar of sorts within the brain’s representation

of the world. Our actions follow the

displacements of the avatar in the brain’s maps of

the environment mediated by the senses. In

actual dreams, when the senses are dormant, the

self moves through a recreated world made of

collages of memories patched through internal

logics.

 Sensations, including the elusive selfawareness, are what reflexive loops feel like in

order to help us navigate. Pain and pleasure are

guiding sensations. Self-awareness is perhaps the

most complex manifestation of this cybernetic

system.

In A Universe of Consciousness, Edelman

describes a reflexive mechanism at work in our

own consciousness. It is a signaling process that

takes place along reciprocal connections. He

calls it “reentry.” Edelman sees reentry as the key

mechanism that binds all our cognitive

mechanisms into a cohesive self. He considers

this massively parallel function to be the uniquely

distinguishing feature of higher brains. But rather

than use reentry as a feature that differentiates

higher from lower brains, whatever that could be,

we can assume that reentry is to varying degrees

a feature of any brain. This feature then can help

in the more general modeling of reflexive

interaction.

 Reflexive neural interaction works within the

complex topologies of our brain to create the

sense of self out of weaving memories. Edelman

suggests that memory is creative rather than
replicative: “every act of memory is, to some

degree, an act of imagination” [3]. Memory for

Edelman is a pragmatic process that always

remembers in and from the present. It is simply

the ability of an agent to repeat or suppress an

action. This ability seems to be at the heart of the

sense of self.

 When we weave together the work of Llinás

and Edelman, we get a rich model of the form

and function of the self. According to their

combined views, then, it is the reflexivity of the

self that would allow autonomous agents to carry

out tasks.

 The question of gauging and fine-tuning

reflexivity with respect to tasks is more

complicated conceptually. Llinás speculates that

our sense of self and what could be called

“intelligence” may well be an emergent property

of how our brain wired itself as a navigational

tool. He concludes that there are many possible

architectures for cognition. Ours does not have

to be the only one. In this case, the evaluation of

intelligence may have to be done with respect to

each separate architecture. In other words, there

are multiple intelligences. In this case, Howard

Gardner has shown that we cannot have a single

measure for all of them. He believes that the

notion of assessment has to be reinvented and

suggests using simulations to gauge how

individuals perform in more realistic and

diversified situations rather than using

standardized metrics for all intelligences [4]. This

implies that for gauging the performance of

autonomous agents with respect to realistic tasks,

simulations rather than metrics should be used.

2. ADAPTATION, SELECTION,

IMITATION, AND PLAY

Piaget presented adaptive behavior as a

combination of accommodation and assimilation.

Pure accommodation is imitation [5]. Pure

assimilation is play. In accommodation, the

individual seeks to copy a situation that calls for

adaptation and changes following the rules of the

external situation. In assimilation, the individual

plays with the situation and tries changes it in

order to embrace it. In other words, in imitation

the individual tends to change the most in the

process of copying, representing, or following

external imperatives. In play, the person jiggles

the external situation and changes it in order to

absorb it. Piaget indicated that adaptation

involves varying combination of those two

extremes.

 A key virtue of Piaget’s model is that it

incorporates naturally the function of play.

Imitation has received plenty of attention, but

play tends to be underestimated or ignored

altogether. Yet it is a key element in agent

autonomy, interaction, and development.

 A question then comes up. How do imitation

and play help stimulate reflexivity in agents?

 Play involves a reconfiguration of elements

being assimilated. This means that there is a

certain metamorphosis at work with the play

elements. They are rearranged until something

happens. This interaction produces a new

meaningful weaving, a new order, a variation, or

what could even be seen from the vantage point

of a previous order as imperfection or error

within the new configuration. But this

imperfection becomes innovation when seen from

the reconfigured perspective. Such is the creative

work of play.

 It is important to recognize at this point that

adaptation or selection in nature do not yield an

exclusive match between the selected agent and

the environment. Adaptation does not produce a

fittest agent. Edelman has noted that selectional

or adaptive systems share a remarkable

property: they can use many structurally different

ways to achieve similar results. He gave this

property the unfortunate name of “degeneracy.”

We can call it diversification. In evolutionary

terms, nature seems to play out all possibilities
that yield viable results. Nature tends to favor

multiple adaptive solutions. Play is what helps us

try out all possibilities. Play is an engine of

diversification. Its presence in Piaget’s model of

adaptation favors the use of multiple points of

view or different approaches in the construction

of autonomous agents for a given task.

 Returning to the relation between play and

reflexive interaction, we see that play with its

tendency for overflowing boundaries, testing

constraints, and diversifying, tends to excite the

agent so that it has to constantly readapt. This

exercises reflexivity.

 But play can make or break an agent. Play

needs to be bounded somehow so that it allows

the agent to exercise its reflexivity without

pushing it past a breaking point.

 Imitation, on the other hand, is linked with

representation. This helps the agent map its

environment in connection with given tasks. We

need to distinguish, however, between copying

structures imitated, and transforming them into

maps based on the agent’s system of

representation. Imitation for autonomous agents

is then a transformation and a translation from

something perceived outside of the agent to

something inside the agent that allows it to

interact more effectively with what is perceived.

Imitation makes maps that are webs of

memories. The sense of memory is the one we

saw before based on Edelman’s view. It is nonrepresentational. Woven memories, although not

a copy of what is perceived, do evoke it in a

functional way. The agent uses the map of

memories to help with navigation, placing itself in

it.

 We can take a closer look at the form and

function of play in a created autonomous agent.

How would it work? Could play have a

purpose? First of all, it is important to recognize

that play is interactive. It does not rest entirely on

the side of the player. The player needs a

partner. That partner is outside the player. This is

perhaps the single most reason why play has

been mostly overlooked before in its cognitive

function. It has not been noticed that there is play

in the environment. Natural environments give us

room to play. They invite play. That may be why

children play. As adults we tend to play less

because we need to function in created structures

that are often set in their ways and restrict play.

These created structures lack the flexibility of

natural environments. Our constructed

environments do not allow for play, unless they

are playgrounds or have been designated as toys.

Our non-artistic creations come with built-in

purposes. Deviations from expected uses are

usually not welcomed.

 How could play enter into autonomous agent

design? What enhancement of the agent could it

bring about? To think about play in relation to

created autonomous agents we need to have an

uncertain environment to begin with. If everything

in the environment is determined, if rules of

operation are fixed, if goals are absolute, then

there is no room to play. But if rather than goals

we think of tasks, if the journey is at least as

important as the destination, and if the

environment has the potential for surprises, then

we can think about play.

 Surprise is the order of the day in

laboratories, for example. It is unfortunate that

theorist shun Murphy’s laws. Can a created

autonomous agent also play when given a task in

an uncertain environment? How can we design it

with that type of intelligence? How do we gauge

the ludic capabilities of an agent? The

introduction of flexibility into designs is a first

step. It is a passive response to play. The next

step is to design agents that can assimilate as they

play. I think we don’t even have preliminary

solutions modeled after these questions because

play has hardly been a factor in design. But we

can make some observations.

 First of all, the agent has to be able to alter

rules. Secondly, the agent has to have tools that
can be used in unintended ways. Let’s consider

now Piaget’s sense of play as assimilation of the

environment to the individual’s existing structures.

For an agent, those structures have to be open,

flexible, so that they can interact in unexpected

ways. Secondly, the agent’s tasks have to be

defined in fuzzy terms so that there is room to

play. Perhaps finally, operating rules can be

allowed to yield new tentative combinations that

could yield unexpected results. The original rules

should not be discarded as new ones emerge.

They all go into a widening repertory of

behaviors and models.

 We can say then that the agent learns through

play about its own system and about the

environment. Assessment of the ludic side of an

agent could then be linked to the quality of what

it learns with respect to very broadly specified

tasks. Contrary to what is often said, play does

have a non-trivial function. In a created

autonomous agent this function could be the

performance of self-motivated activities in an

environment that invites tinkering and exploration.

This generates discovery and learning new ways.

 The value of play is well understood in the

arts. An actor plays a role because there is room

for interpretation and self-expression that can

yield surprise and improve the performance as

gauged by audience response. Salvador Dalí

used to say that to innovate one must first master

previous techniques. When we play with

techniques and tools using pre-existing

knowledge, then something new can emerge

within that set of elements. Picasso liked to point

out that painting wins in the end—not the painter.

This underscores that the agent at play cannot

have full control of the actions. The agent opens

up and exposes itself to the environment to invite

the unknown in and play with it. The writer Annie

Dillard perhaps summed this best by observing

that the art object “is a cognitive instrument

which presents to us, in a stilled and enduring

context, a model of previously unarticulated or

unavailable relationships among ideas and

materials” [6].

 Perhaps we can use more effectively as a

model for created agents Llinás’ conclusion that

we are dreams guided by the senses. The

construction of the agent’s self has to incorporate

internal reflexivity. It has to allow somehow for

self-creation using Piaget’s sense of assimilation.

Play allows created agents to have autonomy in

uncertain environments. The internal structure of

the agent has to be able to learn from such play

and place in memory what it considers valuable.

An agent’s cognitive structure can be designed

so that it recognizes new objects by playing with

them to detect actions associated with the object

and turning such associations into usable

knowledge. Play, then, is a feedforward

interactive behavior: it tests and tags new objects

through tinkering. It has a quick trial-and-error

component that can probe the environment and

see what fits the agent’s tasks and behaviors.

Play can be seen as a form of communication

with the uncertain and the unknown.

3. DISPLACEMENTS

Finally, I would like to touch upon issues of agent

displacement as they relate to play. What selfmotivates autonomous agents move or change?

Conversely, what would prevent autonomous

agents from drifting away from preset tasks?

Since we are focusing on autonomy, we can

exclude direct external influences such as

instructions given periodically to the agent, or

built-in engines.

 We can imagine that two factors may affect

autonomous agents. One is passive and due to

the change of the external environment because

this would tend to affect the agent’s functioning.

The other is internal change that comes about as

the agent interacts in new ways with the

environment. This second factor may be active if
it becomes entangled with the agent’s sense of

self and the agent feels in control of changes.

 Stuart Kauffman proposed in Investigations

that biospheres are constantly reorganizing and

innovating [7]. He noted that within this uncertain

environment, autonomous agents have the

tendency to propagate their systems of

organization into adjacent possibilities, and create

new order. Of course, such displacements and

interactions would affect to some degree the

agent’s internal systems. The broad implication is

that nature may be constructing itself through the

interactions of autonomous agents. But Kauffman

is not suggesting that an evolutionary vector is at

work here. This process of creation is a natural

drift, as Francisco Varela once proposed [8].

We can see it as the result of play from the part

of agents.

 Nature seems to go for viability rather than

optimization. This may well be because in a

complex environment it is simply impossible to

optimize, particularly when there is interaction

between guests and host. The way to proceed is

to diversify viable options in a given environment

and let them evolve. This is where the function of

play becomes critical: it stimulates diversification.

From a design perspective, we can call this

multiple modeling. This agrees with what Llinás

indicated for cognitive systems: they can have

many possible architectures.

 The difference between evolution and drift

may well be mostly a matter of perspective. As

Varela noted, Darwinian evolution favors

optimization, whereas natural drift calls only for

viability. But viability becomes optimization when

selection criteria become so stringent that there is

only one option left in the end. For practical

purposes, it is better to require the less stringent

test of viability. This gives the agent more room

to play. Optimization needs a clear definition of a

landscape in the first place, which in a natural

environment is a daunting if not impossible task.

Viability does not. It is self-testing, so to speak.

 This suggests that performance evaluations of

autonomous agents in a natural environment or

any other environment subject to unpredictable

changes should be based on viability rather than

optimization. One possibility is to gauge the

quality of play by the number of viable solutions

that an agent can produce for a given task in an

environment. Control or enhancement of the

agent’s displacements may affect its

performance. Running models to tweak their

parameters may help gauge such displacements

and fine-tune them for specific tasks. Play control

would require building boundaries that focus and

restrict interactions, as well as insulate the agent

from external changes. Play enhancements would

come about by opening boundaries to give the

agent more freedom in certain chosen directions.

 Play then fuels reflexive interactions between

the agent and its environment, as well as between

the agent’s self and its maps. These mechanisms

can help the autonomous agent adapt to an

environment to carry out tasks that yield viable

behaviors and outcomes.

4. REFERENCES

[1] Arata, Luis O. "Reflections about

Interactivity," 1999. Internet location:

http://media-in-transition.mit.edu/articles/index

arata.html.

[2] Llinás, Rodolfo. I of the Vortex: From

Neurons to Self. Cambridge: MIT Press,

2001. P. 17.

[3] Edelman, Gerald M. and Giulio Tononi. A

Universe of Consciousness: How

Matter Becomes Imagination. New York:

Basic Books, 2000. P. 101.
[4] Gardner, Howard. Intelligences Reframed:

Multiple Intelligences for the 21st Century.

New York: Basic Books, 1999. P. 207-10.

[5] Piaget, Jean. Play, Dreams, and Imitation

in Childhood. New York: Norton, 1962.

[6] Dillard, Annie. Living by Fiction. New

York: Harper & Row, 1982. P. 184.

[7] Kauffman, Stuart. Investigations. New

York: Oxford, 2000.

[8] Varela, Francisco, Evan Thompson, and

Eleanor Rosch. The Embodied Mind.

Cambridge: MIT Press, 1991.