COGNITIVE

PERSPECTIVES ON ROBOT BEHAVIOR

∗

Erik A. Billing

Department of Computing Science, Umeå University, Umeå, Sweden

Keywords:

Behavior based control, Cognitive artiﬁcial intelligence, Distributed cognition, Ontology, Reactive robotics,

Sensory-motor coordination, Situated action.

Abstract:

A growing body of research within the ﬁeld of intelligent robotics argues for a view of intelligence drastically

different from classical artiﬁcial intelligence and cognitive science. The holistic and embodied ideas expressed

by this research promote the view that intelligence is an emergent phenomenon. Similar perspectives, where

numerous interactions within the system lead to emergent properties and cognitive abilities beyond that of the

individual parts, can be found within many scientiﬁc ﬁelds. With the goal of understanding how behavior

may be represented in robots, the present review tries to grasp what this notion of emergence really means

and compare it with a selection of theories developed for analysis of human cognition, including the extended

mind, distributed cognition and situated action. These theories reveal a view of intelligence where common

notions of objects, goals, language and reasoning have to be rethought. A view where behavior, as well as

the agent as such, is deﬁned by the observer rather than given by their nature. Structures in the environment

emerge by interaction rather than recognized. In such a view, the fundamental question is how emergent

systems appear and develop, and how they may be controlled.

1 INTRODUCTION

During the last decades, intelligent robotics has drawn

towards a pragmatic view where no single design phi-

losophy is clearly dominating. On the one hand, low

level interaction with the world is often implemented

with a reactive design philosophy inspired by Rodney

Brooks’ work, (Brooks, 1986; Brooks, 1990; Brooks,

1991a; Brooks, 1991b). On the other hand, classi-

cal AI-elements such as cartographers and planners

are common modules for the high level control. Si-

multaneously, increasing system size and complexity

raises requirements on well structured and modular

system designs. Colored by an object-oriented pro-

gramming approach, the system behavior is imple-

mented through composition of modules. This kind of

systems is commonly referred to as hybrid architec-

tures. (Gowdy, 2000; Murphy, 2000; Doherty et al.,

2004)

In a wider perspective hybrid systems propose a

∗

arts of this text also appear as a technical report:

Billing, E. A. (2007). Representing Behavior - Distributed

theories in a context of robotics, UMINF 07.25, Department

of Computing Science, Umeå University, Sweden.

view of intelligence where simple behavior, like walk-

ing or grasping objects are typically reactive, while

more complex tasks, like choosing a path or select-

ing objects are products of reasoning upon internal

representation. “The robot can think in terms of a

closed world, while it acts in an open world”, (Mur-

phy, 2000).

This view is not totally distant from the one pro-

posed by modern cognitive science. The information

processing model is still dominant for describing high

level cognition (Stillings et al., 1995), while more

reactive models have become popular for describing

lower levels of control, especially within cognitive

neuroscience (Shea and Wulf, 1995; Kaiser and Dill-

mann, 1996).

Even though hybrid architectures are today clearly

dominating the ﬁeld of intelligent robotics, there are

several alternatives. A fundamentally different stand-

point is taken by researchers proposing an embod-

ied and holistic approach, (Matari

c, 1997; Pfeifer and

Scheier, 1997; Pfeifer and Scheier, 2001; Nicolescu,

2003). As these theories enforce concepts of dis-

tributed and emergent behavior, the present work is

an attempt to analyze these notions of emergence ac-

tually mean. Similar ideas can be found within a vari-

373

A. Billing E. (2010).

COGNITIVE PERSPECTIVES ON ROBOT BEHAVIOR.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence, pages 373-382

DOI: 10.5220/0002782103730382

 SciTePress

ety of ﬁelds, including the extended mind (Clark and

Chalmers, 1998), distributed cognition (Hutchins,

1995) and situated action (Suchman, 1987). All these

theories are, in a general sense, studies of behavior

beyond that of the individual, in groups or in inter-

action with artifacts. As such, these theories provide

new perspectives on what it is we are in fact trying to

achieve when building intelligent robots, and how we

should get there.

1.1 Differences Between the Reactive

and Deliberative Views

The reactive view grew during the 1980 as a re-

action against classical artiﬁcial intelligence and

cognitive science, and was in many ways a step

back towards behavioristic ideas, (Braitenberg, 1986;

Brooks, 1986; Georgeff and Lansky, 1987; Maes,

1991). The early reactive trend argued strongly

against representations, but due to the obvious limi-

tations of such an attitude, later work within the reac-

tive ﬁeld incorporate representations, but of a differ-

ent type than the ones typically found within deliber-

ating systems.

Deliberative architectures implement a domain

ontology, that is, a deﬁnition of what things that exist

in the world, but without a precise description of their

properties and interrelations, (Russell and Norvig,

1995). This corresponds to a reductionist perspective

also found within cognitive science and classical arti-

ﬁcial intelligence.

Reactive systems are instead deﬁned by a low-

level speciﬁcation that corresponds to the inputs and

outputs of the system, referred to as the sensory-

motor space (Pfeifer and Scheier, 1997). I here

refer to a quite large variety of approaches, in-

cluding the subsumption architecture (Brooks, 1986;

Brooks, 1991b), behavior based systems (Matari

1997; Arkin, 1998; Nicolescu, 2003) and sensory-

motor coordination (Pfeifer and Scheier, 2001; II and

Campbell, 2003; Bovet and Pfeifer, 2005). Without

claiming that all these approaches are one and the

same, I use the term reactive as a common notion for

these approaches proposing an embodied and holistic

view.

The low-level speciﬁcation deﬁnes the sensory-

motor space as an entity which is related to the ex-

ternal world via a physical sensor or actuator on the

robot. For a simple robot with eight proximity sen-

sors and two independently controlled wheels, the

sensory-motor space is ten-dimensional where each

dimension corresponds to one sensor or motor. Many

sensors provide multi-dimensional data. For example,

a camera with a resolution of 100x100 pixels would

increase the sensory-motor space with 10 000 new

dimensions, where each pixel in the camera image

corresponds to one dimension in the sensory-motor

space. Cameras and other complex sensors could also

be viewed as providing a single, complex, dimen-

sion in the sensory-motor space, but the amount of

pre-processing or interpretation of data is always very

limited in a low-level speciﬁcation implement.

Pfeifer and Scheier (Pfeifer and Scheier, 1997)

point out that a system using a low-level speciﬁca-

tion has a much larger input space than deliberative

systems speciﬁed by a domain ontology, which also

allows much greater complexity. Another interesting

difference lies in information content. On the one

hand, each dimension in the input space of a delib-

erative system is fairly informative. It could be the

horizontal position of the robot on a map or the height

of an object in front of the robot. On the other hand,

most dimensions in the sensory-motor space are es-

sentially meaningless if not viewed in the context of

other dimensions. A single pixel in an image says

very little about the content of the scene when that

pixel is viewed alone, but in the context of the other

pixels, it may be very informative. One could easily

argue that such a large and complex sensory-motor

space is the result of an ill chosen representation.

With no doubt it is much easier to create readable

representations using a deliberative approach, where

sensor data have been processed so that it much better

reﬂects our own understanding of what is going on.

Even though this criticism is correct and impor-

tant, the sensory-motor space should not be under-

stood as an unprocessed version of objects and other

aspects in the world, but the representation of some-

thing else. The original argument against represen-

tations found in early reactive research has the last

decade been replaced with a more accepting attitude

towards representations, but representations of behav-

ior rather than representations of the world. Accord-

ing to Pfeifer and Scheier, many things can be solved

in a much simpler and more robust way without the

use of high-level percepts. In general, the sensory-

motor space appears to be a more suitable frame for

representations than the kind of world models found

in classical AI. (Pfeifer and Scheier, 1997; Pfeifer and

Scheier, 2001; Dawson, 2002)

Low-level speciﬁcations have the great advantage

that each dimension in the sensory-motor space is di-

rectly mapped to the corresponding sensors or motors,

while the inputs to a deliberative system, such as posi-

tion and size of objects, are often very hard to acquire.

By assuming the necessity of high-level percepts we

impose our own frame of reference upon the agent.

Our notions of objects and states in the world are for

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374

sure handy when reﬂecting upon an agent’s behav-

ior, but may not be necessary, or even desirable, when

performing the same acts.

The principle of the frame of reference may be il-

lustrated through the parable with the ant, presented

by Herbert A. Simon, (Simon, 1969). Imagine an ant

making its way over the beach, and that the way it

chose was traced. When observing all the twists and

turns the ant made, one may be tempted to infer a

fairly complex internal navigation process. However,

the complexity of the path may not be the result of

the complexity of the ant, but the result of interac-

tion between a relatively simple control system, and a

complex environment.

Long before Brooks presented his ideas on reac-

tive robotics (Brooks, 1986; Brooks, 1990; Brooks,

1991b), it was shown that complex behavior could

emerge from simple systems, for example through the

Homeostat (Ashby, 1960) and Machina speculatrix

(Walter, 1963). Furthermore, Braitenberg’s Vehicles

(Braitenberg, 1986) was one of the most important in-

spiration sources for Brooks’s work.

This discussion constitutes a central part of the

criticism against deliberative systems and the motiva-

tion for a reactive approach. However, since reactive

systems do not deﬁne any ontology with meaning-

ful inputs, many types of, typically sequential tasks,

are very hard to represent in this manner. Even

though several examples of reactive systems show-

ing deliberative-like behaviors exist, for example Toto

(Matari

c, 1992) and the reactive categorization robot

by Pfeifer and Scheier (Pfeifer and Scheier, 1997),

both the systems and the task they solve are typically

handcrafted, making them appear more as cute exam-

ples of clever design than solutions to a real problem.

The difference between reactive and deliberative

systems has been described as the amount of compu-

tation performed at run-time, (Matari

c, 1997). A reac-

tive control system can be derived from a planner, by

computing all possible plans off-line beforehand, and

in this way create a universal plan (Schoppers, 1987).

This argument about on-line computation beauti-

fully points out how similar the two approaches of re-

active and deliberative control may be. Still, when

proposing the reactive approach, Rodney Brooks

pointed out a number of behavioral differences to

classical deliberative systems: “robots should be sim-

ple in nature but ﬂexible in behavior, capable of act-

ing autonomously over long periods of time in uncer-

tain, noisy, realistic, and changing worlds”, (Brooks,

1986). So if a reactive controller is merely a pre-

computed plan, why these differences in behavior?

One critical issue is speed. Brooks often points

out the importance of real-time response and that the

cheap design of reactive systems allows much faster

connections between sensors and actuators than the

deliberative planners, (Brooks, 1990). Even though

this was an important point in the early nineties, the

last years’ increase in computational power allows

continuous re-planning within a reactive time frame,

(Dawson, 2002).

Another reason may be that reactive controllers

are typically not derived from planners. Rather, reac-

tive controllers are handcrafted solutions specialized

for a certain type of robot. Achieving a speciﬁc com-

plex behavior in a reactive manner can be a challenge,

which may be one important reason for the limited

success of reactive systems in solving more complex,

sequential tasks (Nicolescu, 2003). Taking Matari

c’s

point about run-time computation into account, the re-

active approach still does not propose a clear way to

achieve a desired controller; it only shows that the de-

liberative part can be removed when intelligence has

been compiled into reactive decision rules.

Hybrid systems do obstacle avoidance using reac-

tive controllers not because re-planning is computa-

tionally heavy, but because re-planning is difﬁcult to

implement. Even though one could imagine a plan-

ner generating exactly the same behavior as one of

Braitenberg’s vehicles avoiding obstacles, the struc-

ture of such a planner would probably be much more

complicated than the corresponding controller formu-

lated in reactive terms. This may in fact, at least

from an engineer’s point of view, be the most suit-

able distinction between the reactive and deliberative

perspectives. It appears that behaviors like obstacle

avoidance and corridor following is easily formulated

in reactive terms, while selecting a suitable path from

a known map is better formulated using a planner.

Other things, actually most things, are too hard to

manually design using any of these two approaches.

1.2 Emergence of Behavior

As mentioned in the previous section, supporters of

the reactive approach freely admit that the implemen-

tation of high-level deliberative-like skills in reactive

systems is very difﬁcult, (Pfeifer and Scheier, 2001;

Nicolescu, 2003). The route to success is often said

to be emergence, (Maes, 1990; Matari

c, 1997; Pfeifer

and Scheier, 1997). But what exactly does this mean?

The term emergent is commonly described as

something that is more than the sum of its parts, but

apart from that it is in fact hard to arrive at a deﬁni-

tion suitable for all uses of the term, (Corning, 2002).

Within the ﬁeld of intelligent robotics, emergence is

used to point out that a robot’s behavior is not explic-

itly deﬁned in the controller, but something that ap-

COGNITIVE PERSPECTIVES ON ROBOT BEHAVIOR

375

pears in the interaction between the robot and its en-

vironment. Pfeifer and Scheier (Pfeifer and Scheier,

2001) proposes a number of design principles for au-

tonomous robots. The critical points are shortly sum-

marized below.

1. Behavior should emerge out of a large number of

parallel, loosely coupled processes.

2. Intelligence is to be conceived as sensory-motor

coordination, i.e., the sensory-motor space serves

as a structure for all representations, including the

categorization and memory.

3. The system should employ a cheap design and ex-

ploit the physics of its ecological niche.

4. The system must be redundant.

5. The system should employ the principles of self-

organization.

Not surprisingly, those principles are well aligned

with those found in literature discussing emergence,

(Corning, 2002; Flake, 1998). Consequently, mod-

ern reactive architectures should constitute a good

approach for design of systems showing emergent

properties, but this is yet far from a uniﬁed theory

on which robotics architectures could be built, (Hey-

lighen et al., 2004). Before a theory of emergent be-

havior could actually be used, much more has to be

understood about the theoretical properties of emer-

gence, but such an analysis is seldom found in robotic

literature.

1.3 Criticism Against the Hybrid View

After looking a bit closer at the principles of the reac-

tive and deliberative approaches, the philosophy be-

hind hybrid approaches seems to be much closer to

the latter. Hybrid systems clearly align to a reduction-

ist view, enforcing the importance of system modular-

ity and hierarchical structures. The promoters of hy-

brid systems motivate this design effort in completely

different ways than the supporters of reactive systems

argue for a holistic perspective. Obviously, from an

engineering perspective, it is very important to be able

to build larger systems in some kind of modules, so

that each part can be tested and reﬁned separately.

While this strongly contradicts the holistic perspec-

tive, reactive supporters have no solution to, and are

generally not interested in, these issues.

So what exactly are the problems with combining

the reactive in the small, and deliberative in the large?

Since the hybrid approach is so wide and generally

friendly towards anything that works, it is hard to ac-

tually say something about these systems which truly

applies to all of them. Still, some common criticism

has been raised against the hierarchical approach, es-

pecially from the ﬁeld of embodied cognitive science.

The core issues are summarized bellow.

• Even though hybrid systems adopt an embod-

ied view for interaction with the world, they still de-

ﬁne a domain ontology and are consequently bound

by the limitations of this approach. One critical point

of the reactive approach is that no concepts or sym-

bols should be pre-deﬁned. This point is lost when

hybrid systems use reactivity as an interface to the

world rather than the source of intelligence. (Pfeifer

and Scheier, 2001)

• The kind of information produced by the reac-

tive layer in a hierarchical system is often fundamen-

tally different from that required by the deliberative

subsystems, making it hard to design suitable interac-

tion between the two layers. For this reason, the sens-

ing part in the deliberative layer is often designed in a

non-reactive way, reintroducing the problem of how

objects, and concepts in general, should be recog-

nized in complex and noisy data. (Pfeifer and Scheier,

2001; Dawson, 2002)

• While a critical aspect of modularity is to be able

to test and control the function of each module before

inserting it in the complete system, one important goal

of reactive approaches is to achieve emergent proper-

ties which by deﬁnition do not appear in the modules

alone. Even though hybrid systems successfully em-

ploy simpler reactive behavior, they do not leave room

for emergent properties. (Pfeifer and Scheier, 2001;

Brugali and Salvaneschi, 2006)

2 AN EXTENDED PERSPECTIVE

The fundamental differences between modern ap-

proaches within intelligent robotics have now been

outlined. The rest of this paper present a number of

different views on cognition, and apply them in a con-

text of intelligent robotics. These views will make

deliberative and reactive perspectives appear less like

the two extremes, and more like one dimension within

the multi-dimensional study of cognition and behav-

ior.

Cognitive science has received signiﬁcant

amounts of criticism for its undivided focus on the

individual, where a proper analysis of the social

aspects of interaction is missing, (Greeno, 1993;

Heylighen et al., 2004; Hutchins, 1995; Suchman,

1987). Similar criticism has been raised towards

classical AI and deliberative robotics, but to some

extent it also applies to reactive systems. Both

deliberative and reactive approaches share a view of

the single agent as one conceived unit, interacting

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with the outside world through input and output

interfaces. Even though this might seam like a safe

assumption for many systems like humans, animals,

computers and robots, I will in this chapter present

a couple of theories where the object of analysis is

changed to incorporate fundamentally different types

of cognitive systems. As will be illustrated, many

aspects of these systems are strikingly similar to the

architectures proposed within robotics.

2.1 The Extended Mind

Clark and Chalmers (Clark and Chalmers, 1998) de-

scribe two people, Inga and Otto, who are both going

to visit the Museum of Modern Art which lies in the

53rd street. Inga heard from a friend that there is an

exhibition at the Museum of Modern Art and she de-

cides to go there. She looks up the address and re-

members it. She forms the belief that the Museum

of Modern Art is on 53rd street. But now consider

Otto who has Alzheimer’s disease and instead of re-

membering the address writes it down in his note-

book. Clark and Chalmers argue that Inga and Otto

in principle have the same belief, even though parts

of Otto’s belief in a very strong sense are outside his

body. This is an example of the extended mind.

Interestingly, Otto’s behavior may easily be de-

scribed in reactive terms. Otto changes the environ-

ment, his notebook, in a manner which later will lead

him to the correct address. In contrast, Inga’s behav-

ior is a classical example which can’t be described

within a purely reactive architecture. Still, Clark and

Chalmers point out that Otto’s and Inga’s cognitive

processes are essentially the same. I would argue that

the reason why we usually view Inga’s and Otto’s

cognitive processes as quite different is our usual con-

cept of an individual. If we chose to view the per-

son as an entity enclosed by the skin, the use of a

notebook as memory is very different from the use

of nerve structures for the same purpose. But, if

we instead follow Clark and Chalmers’ argument and

widen our notion of a person to include the notebook,

the two types of memory appear very similar.

This point could also be illustrated by a computer.

What exactly is a computer? Most people would

probably say that it’s the screen, the key-board and

mouse, and of course the box on the ﬂoor which you

connect all the cables to. If one uses a Memory Stick

to store things on, that is not a part of the computer,

but a different object. Still, technically speaking, the

Memory Stick, when connected, is very similar to the

hard drive within the computer. The Memory Stick

might, just as Otto’s notebook, have lower storage ca-

pacity, a bit slower access speed, and not always be

available. Still, it ﬁlls the same function as the in-

ternal memory. Admittedly, our common notion of a

person, where the notebook is not included, is very

convenient, but we should be aware that it is merely a

convention.

This discussion opens up the notion of an agent.

We choose to see one agent as separated from its en-

vironment not because it is different from the environ-

ment, but because it, from our perspective, is conve-

nient to view it like that. This does not imply that the

notion of agents and objects is totally arbitrary, but

neither is it totally predeﬁned.

I mentioned earlier Pfeifer and Scheier’s notion of

frame of reference, pointing out that an agent’s behav-

ior is always seen from an observer’s perspective. The

view presented here takes one step further by saying

that even the notion of agent is dependent of the ob-

server. This distinction is important since Pfeifer and

Scheier strongly argue towards representations within

a sensory-motor space. If the agent is an entity cre-

ated by the observer, so are sensors and motors, and

with these, the sensory-motor space.

Following this discussion, the notion of an agent

should be able to divide into smaller, sub-agents, with

different sensors and motors. The functions of these

sub-agents may differ drastically from the function of

the combined agent, just like Otto and his notebook

can do more things together, than neither of them can

do separately. Consequently, the question of how be-

havior is represented is transformed into how Otto

ﬁgures out that he should use a notebook? Or more

generally: How does purposeful emergent behavior

among agents appear?

2.2 A Universe of Possibilities

One of the inspiration sources to Clark and Chalmers’

work came from distributed cognition, (Hutchins,

1995). Hutchins points out the importance of viewing

cognitive processes as something that goes on both in

the environment and within the individual, but in con-

trast to Clark and Chalmers, Hutchins’ focus lies on

group level dynamics. In this context, the agent, or

the cognitive system, is expanded not only to incor-

porate one person and his tools, but many people and

artifacts in cooperation.

Hutchins takes a few steps further than Clark and

Chalmers by not only proposing an extended view, but

also using it to analyze systems. Distributed cogni-

tion has been applied to many systems, including ship

navigation (Hutchins, 1995), human-computer inter-

action (Hollan et al., 2000), various aspects of air-

plane control (Hutchins and Klausen, 1996; Hutchins

and Holder, 2000; Hutchins et al., 2002) and more re-

COGNITIVE PERSPECTIVES ON ROBOT BEHAVIOR

377

cently clinical systems (Galliers et al., 2006). While

distributed cognition as used in these examples have

no apparent application to robotics, the result of this

research might still shed some light on what we want

to achieve when designing for purposeful emergent

behavior.

From a deliberative perspective, a large and difﬁ-

cult problem is to recognize objects and their proper-

ties in complex and noisy data. From a reactive view,

the same problem is instead described as how to arrive

with suitable emergent properties. And ﬁnally, form

the perspective of distributed cognition, this problem

is strongly related to the formation of interpretations

within a group. Hutchins investigates the properties

of interpretation formation on group level using con-

straint satisfaction networks, (Hutchins, 1995). The

weights of the networks were arranged so that each

network could arrive at only two stable states, or in-

terpretations. One interpretation corresponds to the

activation pattern 111000 while the other interpreta-

tion corresponds to 000111, see Figure 1.

Figure 1: Constraint satisfaction network, (Hutchins, 1995,

p. 244). Black and gray lines represent positive and nega-

tive connections, respectively. Strong activation in left side

nodes will consequently inhibit activation in nodes to the

right, and the other way, driving the network towards one of

two stable states. Republished with permission.

The initial activation of the nodes is here viewed

as conﬁrmation bias. When the network is executed

alone, it will always arrive with the interpretation

closest to its initial state. However, when the net-

works are connected so that the activity of some nodes

propagates to other nodes of a different network, their

decision properties change due to interaction between

the networks. As Hutchins puts it, this illustrates how

interpretation of information changes due to the orga-

nization of the group. In a robotics context, this might

be applied as having several reactive controllers cre-

ating a virtual “environment” for each other. More

speciﬁcally, controllers do not only take input from

sensors and send commands to actuators, but might

also sense and act upon other controllers, drastically

increasing the complexity of the system as a whole.

The organization of such a system should be similar

to the organizational properties of a group.

Hutchins shows that having this kind of organi-

zation, where multiple connected agents try to make

their individual interpretations, produces a system

that efﬁciently explores interpretation space. Further-

more, even after reaching a common interpretation,

such a system is much more likely to re-evaluate the

interpretation in case of new evidence. However, in

case of too much interaction within the group, inter-

pretation space is not explored properly. In contrast,

too little interaction will result in that no single inter-

pretation is achieved. Hutchins calls this the funda-

mental tradeoff in cognitive ecology.

This discussion is not only interesting as analysis

of group behavior, but also as a way to understand in-

terpretations within the individual. Here, the process

of transforming sensor data into symbols with mean-

ing is replaced with a continuous constraint satisfac-

tion between sensors, actuators and internal states.

In this view, we do not perceive what is in the en-

vironment; instead we are striving towards the clos-

est stable state, pushed in one direction or another by

changes in the environment.

There are mainly two advantages with this model

of cognition, compared to the classical view of in-

formation processing. First, the interpretation always

arrives from the current state of the system. Conse-

quently, each interpretation is based on much more

information than when sensor data is seen as a se-

ries of discrete readings which should be understood

more or less separately. Secondly, when the notion of

symbols is replaced with that of attractors, the number

and meaning of these entities can be changed dynam-

ically. This opens up the possibility for a solution to

one key problem of deliberative processing; the cre-

ation of new symbols.

This view of cognition as the propagation of repre-

sentational states across representational media might

provide a new and powerful tool for understanding

interpretation and decision making also in robots.

However, even though Hutchins provides an extensive

analysis of several existing distributed systems, a gen-

eral understanding of how such a system appears and

develops is still missing, (Heylighen et al., 2004).

2.3 Situated Action

When studying interactions between people, the anal-

ysis of language becomes one critical sub ﬁeld. Clas-

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378

sical cognitive science literature describes language

as “a system that uses some physical signal (a sound, a

gesture, a mark on paper) to express meaning”, (Still-

ings et al., 1995). In other words, language is viewed

as a communication channel where some meaning,

i.e. an internal representation, is encoded into a phys-

ical signal using some grammar and then decoded

by the listener into a similar internal representation.

However intuitive this view may appear, it is not the

only one. A fundamentally different perspective was

presented in late eighties by Lucy A. Suchman under

the name situated action, (Suchman, 1987).

Suchman’s claim is that the traditional view of lan-

guage includes several fundamental problems. One

of the most important issues is the discussion around

shared knowledge. If the cognitive view of language

is correct, a speaker must not only encode the repre-

sentation into words, but also take into account what

the listener already knows. As Suchman puts it, this

is equivalent with having a body of shared knowledge

that we assume all individuals within our society to

have. When speaking, only the speciﬁcs of the inter-

nal representation are transformed into words, leaving

out everything covered by the shared knowledge.

To exemplify the problem, Suchman uses the re-

sult from an exercise assigned by Harold Garﬁnkel,

(Suchman, 1987). Students were asked to write down

a description of a simple conversation. On the left

hand side of the paper the students should write what

was said, and on the right hand side what they un-

derstood from the conversation. While the ﬁrst part

of the assignment was of course easy, the second part

seemed to grow without limit. Many students asked

how much they were supposed to write, and when

Garﬁnkel imposed accuracy, clarity and distinctness

the students ﬁnally gave up with the complaint that

the task was impossible. The point here is not that the

body of shared knowledge is too large to write down

on a paper, but that the task resulted in a continually

growing horizon of understandings to be accounted

for.

The assignment, it turned out, was not to describe

some existing content, but to generate it. As such, it

is an endless task. The students’ failure suggests not

that they gave up too soon, but that what they were as-

signed to do was not what the participants in the con-

versation themselves did in order to achieve shared

understanding. (Suchman, 1987).

Even though there might be several other ways to ex-

plain the result from Garﬁnkel’s assignment, Such-

man’s point is striking. If knowledge is not pre-

existing to language as much as it is generated by it,

it puts our understanding of internal representations

in a fundamentally different light. The meaning of a

spoken phrase does not appear to exist in any stronger

sense than obstacles exists for one of Braitenberg’s

vehicles.

Situated action is not at all limited to analysis of

language. In fact, situated action tries to unify all

kinds of behavior where language is seen as a very

specialized sub ﬁeld. In such a view, spoken words

has the same relation to semantics as an agent’s ac-

tions has to intentions. However, it should be remem-

bered that Suchman’s work is presented as a theory

of human-computer interaction rather than a theory

of behavior or intelligence.

If language and other complex behavior do not be-

gin with an internal representation or intent, how is it

produced? The view proposed by Suchman begins

in the context of the agent: “every course of action

depends in essential ways upon its material and so-

cial circumstances,” (Suchman, 1987). The circum-

stances or situation of actions can, at least in a con-

text of intelligibility, be deﬁned as “the full range of

resources that the actor has available to convey the

signiﬁcance of his or her own actions, and to interpret

the actions of others,” (Suchman, 1987). This could

be interpreted as if the world is understood in terms

of actions. The fact that we know how to walk makes

us really good at recognizing such behavior. In the

domain of human-computer interaction, the same ar-

gument implies that our understanding of a computer

is represented in terms of what we can do with it, not

as a structural model of the computer as such. As a

consequence, a selection of the best path towards a

desired goal is not dependent on a representation of

roads, but on the availability of the actions for turning

left or right.

Furthermore, the goal of situated action is not rep-

resented in any other way than as preferences to some

actions given a speciﬁc situation. Our common under-

standing of plans and goals is in this context nothing

but a way to reﬂect on past events. As Suchman points

out, a declaration of intent generally says very little

about the precise actions to follow, it is the obscurity

of plans that makes them so useful for everyday com-

munication (Suchman, 1987).

This discussion puts not only notions of inten-

tions and plans in a secondary position, but conscious

thoughts in general appear to be less the driving force

behind action than an artifact of our reasoning about

action. Seen in the robotics context, deliberative pro-

cesses should in a very strict sense be emerging from

lower levels of interaction, not something predeﬁned

that supervises the lower levels.

One interesting implication of these theories is

that observed sensor data bears a very loose connec-

tion to its semantic content. The interpretation is cre-

COGNITIVE PERSPECTIVES ON ROBOT BEHAVIOR

379

ated by the observer in interaction with the data rather

than extracted from the observed data. The creation

of an interpretation is in this view more about gener-

ating information, than processing it.

3 DEVELOPMENT AND DESIGN

The theories presented above depict a perspective of

intelligence where cognitive ability emerges out of in-

teractions between multiple parts of an agent. The

agent is very loosely deﬁned as a cognitive system,

i.e., a large number of physically and/or socially dis-

tributed entities which interact and in this way achieve

something more than any of them could do alone.

More explicitly, intelligent behavior of a cognitive

system is produced from entities which are totally un-

aware of the dynamics of that system as a whole.

In such a view, elements in the world are never

explicitly represented, but appear in terms of possi-

bilities for situated actions. Information does not ﬂow

from inputs to outputs, but back and forth through nu-

merous representational states, coordinating sensors

and actuators rather than controlling them. The mean-

ing of actions, symbols or data in general is achieved

through interaction among elements, not given by a

grammar. Conscious processes are not the fundamen-

tal foundation for intelligent behavior, but its funda-

mental phenomenon. The question still remaining is:

How does one create a system based on the principles

presented above?

3.1 Evolution of Self-organization

Emergent properties which in the previous discussion

so gracefully are said to explain intelligence do, from

an engineering perspective, often cause more prob-

lems than they solve. What is seldom mentioned is

that guidelines like those presented by Pfeifer and

Scheier, (Pfeifer and Scheier, 2001) only address

half the question of emergent behavior. We are nor-

mally not interested in just any emergent behavior, but

speciﬁcally in those emergent effects which fulﬁll the

task for which the robot is designed. This may prove

to be much more difﬁcult to achieve than just emer-

gence in general.

There are a number of theories approaching this

problem. The most frequently mentioned within

robotics is self organization. The principle of self or-

ganization means that the system spontaneously de-

velops functional structure through numerous interac-

tions between its parts. The basic mechanism behind

this structure is mutual beneﬁt, symbiosis. Parts in

the system will continue to rearrange until both ﬁnd a

relative state which is satisfactory. A frequently used

interaction pathway will grow stronger while rarely

used pathways will weaken or disappear. The mech-

anisms controlling what is satisfactory will as a con-

sequence have direct inﬂuence on the emergent prop-

erties of the system as a whole. (Heylighen and Ger-

shenson, 2003)

While the principle of self-organization provides a

clearer understanding of the mechanisms controlling

emergence, it still does not explain how useful behav-

ior emerges. The fact that favorable interactions are

reinforced on the micro level will certainly not lead

directly to favorable behavior on the macro level.

For natural systems the obvious answer is evolu-

tion. The fundamental mechanism of natural selection

will, given enough time, lead to a solution. However,

this gives us little hope for training robots. The prob-

lem space for a robot acting in the real world grows

extremely fast. Allowing a robot to try out a pop-

ulation of randomly chosen behaviors will, even for

very simple problems, most likely never lead to a so-

lution. Consequently, the evolutionary process won’t

work since nothing can be reinforced.

Interestingly, a robot acting in the real world is,

by deﬁnition, in the same situation as many biologi-

cal systems, which obviously have evolved. The dis-

cussion above makes an evolutionary explanation to

the problem of grabbing and moving objects appear

highly unlikely. Even when including the billions of

years preceding the human era, the chance of com-

bining all the biological structures required for object

manipulation to work appears very small. And yet

evolution has given us a wonderful tool in the form

of the hand, and the neural structures underlying its

control.

The explanation for our highly ﬂexible and dex-

terous ability to manipulate objects is of course that it

did not evolve from nothing. As such, the human hand

is not an optimal solution, nor is it anything close to

optimal. Instead is it a result of what came before it.

Small incremental changes of the mammal front legs,

which at each stage were reinforced through natural

selection, have eventually led to the human arm and

hand. (Wolfram, 2002)

Why this divergent discussion about evolution?

The manipulator of a robot has to be designed. We

are simply interested in teaching the robot to use it,

given a fairly short period of time. The point of this

sidestep into evolution is that the physical shape of the

human hand did not evolve alone, but together with

the neural system controlling it. The human child is

born with a large amount of basic reﬂexes, which are

all fairly simple. In robotic terms, we would proba-

bly call them purely reactive behaviors. (Thelen and

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

380

Bates, 2003; Grupen, 2006)

Some of these innate behaviors are certainly crit-

ical for the survival of the infant, such as the grip re-

ﬂex or the sucking reﬂex. But many other reﬂexes

do not have an obvious purpose, such as the asym-

metric tonic neck reﬂex, landau reﬂex or the galant

reﬂex, (Grupen, 2006). Instead, reﬂexes like these

seem to play a key role for learning. As mentioned

above, the child is born with a set of reﬂexes. These

basic reﬂexes are, during the ﬁrst four years, grad-

ually replaced by new, more complicated behaviors.

The child seams to learn through an evolutionary pro-

cess of behavioral development. New behaviors ap-

pear as a modiﬁcation or combination of more basic

behaviors, while other behaviors disappear. In theo-

retical terms, this incremental development allows the

problem space to remain small even as the problems

grow more complicated. The full space of possible

solutions to the problem is never searched, but only

the parts covered by previous knowledge. Sometimes,

the small changes to the underlying control structure

result in drastic changes in behavior, which we see

in the child as the establishment of new behaviors.

This kind of incremental development process should

favor robust behavior rather than optimal. A behav-

ior which succeeds under many circumstances sim-

ply has much greater chance of survival than a perfect

solution only succeeding under very special circum-

stances.

4 CONCLUSIONS

Through out this review there has been a theme of

emergent behavior. Notions of objects in the world,

goals and concepts in general are said to emerge out

of simpler parts. The literature reviewed here fre-

quently points out several aspects critical for a sys-

tem to show emergent properties. However, it has

been much harder to ﬁnd clear theories of how to con-

trol these emergent properties. In fact, one important

property of emergence seems to be that it can not be

controlled in a supervising way. Without a proper the-

ory of how to arrive at useful emergent properties, the

argument that behavior should emerge is very much

like saying that we do not know. It is generally admit-

ted that distributed and emergent control systems for

robots are very hard both to obtain and control. As

such, these approaches do not seem less problematic

than their counterparts within classical AI.

Nevertheless, the concepts presented in this re-

view open a new frame for representation of behavior.

A troubling and yet thrilling aspect of these theories is

that they span over an enormous theoretical horizon.

Some fundamental problems are solved, many new

are introduced, but just viewing the problem from a

different perspective might get us closer to a general

understanding: An understanding of what intelligence

is and how it can be created.

ACKNOWLEDGEMENTS

I thank Lars-Erik Janlert and Thomas Hellström at the

Department of Computing Science, Umeå University

for valuable input to this work.

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