
BRITISH ASSOCIATION ANNUAL FESTIVAL - PRESS PAPERS FORM

SPEAKER'S NAME:
                Surname:        Sloman

                Forenames:      Aaron

                Title:          Professor
                        of Cognitive Science and Artificial Intelligence

NAME OF ORGANISATION:
                The University of Birmingham

        Address:
                School of Computer Science
                The University of Birmingham
                Edgbaston
                Birmingham B15 2TT

TITLE OF PAPER:
                What kinds of machine can have emotions?


DATE OF PAPER:
                Thursday 12th Sept 1996

TIME OF PAPER:
                1645 (Room: Poynting Lecture Room, Physics)

NOTE 1
The text that follows is a summary not a transcript. The presentation
at the conference made considerable use of diagrams.

See http://www.cs.bham.ac.uk/~axs/misc/emotions.mit96.slides.ps

NOTE 2:
In this plain text version {\em .... } indicates that the text enclosed
should be italicised (emphasised). Also G\"odel represents the name
"Godel" with an umlaut over the "o". The precis follows.

=======================================================================

                WHAT KINDS OF MACHINE CAN HAVE EMOTIONS?

                              Aaron Sloman

                       School of Computer Science

                      The University of Birmingham

                      http://www.cs.bham.ac.uk/~axs


ABSTRACT:
     Work in Artificial Intelligence (AI) provides a new
     framework for thinking about how minds work. The talk will
     show how the framework can accommodate motivational and
     emotional aspects of mind. This is done by relating AI to
     the study of design space, niche space and their
     relationships. We describe three levels of sophistication in
     designs for behaving systems {\em reactive}, {\em deliberative}
     and {\em meta-management} and we outline ways in which they may
     be able to explain both some capabilities which we share
     with other animals and some distinctively human
     capabilities, including forms of conscious and
     ``perturbant'' emotional states. Besides its theoretical
     interest this is potentially of considerable practical
     importance in helping us understand ourselves and perhaps
     helping us to remedy various things that can go wrong.

     So far there is no evidence that these architectures and
     mechanisms depend on extensions to contemporary physics
     such as the quantum gravity theory proposed by Penrose.


INTRODUCTION
This talk is partly intended to be a rebuttal of the sorts of arguments
being put forward by Professor Roger Penrose, who will also be talking
at the BAAS conference in the Brains Minds and Consciousness session on
the same day.

He and others attack the so-called "Strong AI" thesis, one version of
which claims that the sorts of systems being developed within the
discipline of AI can in principle have mental states and processes, such
as believing, desiring, deciding, understanding or being conscious.
(There are other versions which are less interesting.)

[Note: "AI" now often used as a name in its own right was originally an
abbreviation for "Artificial Intelligence". This name is misleading,
however, since the discipline has always been as much concerned with
modelling {\em natural} intelligence in humans and other animals as with
creating useful "smart" artefacts, though some researchers have
concentrated more on modelling humans (e.g. H.A.Simon and A.Newell)
others more on designing artefacts (e.g. John McCarthy, who
invented the name).]

Some of those who attack AI do so on the basis of a famous mathematical
theorem by Kurt G\"odel, his second incompleteness theorem. The theorem
is mathematically complex and its interpretation in this context
disputable. The anti-AI argument depends essentially on the claim that
human mathematicians can see the truth of a complex mathematical formula
that could not be derived by any formal computation satisfying certain
standards of computational rigour. Many reputable mathematicians and
logicians reject the arguments by Penrose and others based on this
theorem. My own refutation of Penrose's version was published in
1992.[Sloman92]

However, since very few human beings, and, as far as I know, no other
animals, can understand G\"odel's theorem, it is very odd to use this
esoteric mathematical capability as the basis for an attack on AI. At
most it would prove that we cannot use AI techniques to produce
artificial mathematicians who are able to reason about infinite sets of
numbers. This would leave open the question whether we can produce
robots that can perceive things, have beliefs, have desires, have
intentions, make plans, learn things, solve problems, be conscious
sometimes and unconscious at others, and be emotional at times, without
being capable of doing advanced mathematics, like a young child and most
adults.

I propose that we should first look at these more basic and widespread
mental capabilities, shared by all human beings, and perhaps many other
animals, and see whether we can understand what sorts of architectures
and mechanisms make them possible, before we try to explain or model
sophisticated mathematical capabilities that we do not share with other
animals and which are not obviously present in all human beings.

It has recently become fashionable to focus on {\em consciousness}, as
if it were a well defined clearly identifiable phenomenon about which it
makes sense to ask questions like: How did it evolve? What are its
functions? Which animals have it? What are its neural correlates? What
sorts of physical machines can produce it? This is another topic on
which Penrose has written much, claiming that "it" can occur only in
physical systems whose operation cannot be explained by contemporary
physics, but requires an extension which links quantum mechanics with
gravitational forces.

Unfortunately, although Penrose may be right that we need to extend
modern physics in order to gain a full understanding of how the brain
works (and there's nothing surprising in the suggestion that our current
knowledge of the fundamental nature of physical reality is still
incomplete), I do not think there is any well defined concept of
consciousness around which it makes sense to focus the questions he and
others wish to raise.

Rather, typical adult human consciousness involves a collection of
different capabilities, different subsets of which can occur in
different people, in different animals, in newborn infants, in brain
damaged people, in cases of senile dementia. Thus there is no single
"it" about which we can sensibly asked how it evolved, what its function
is, etc. (The tendency to think there is a single "it" arises out of
deep philosophical confusions which I shall not discuss here.)


THE DESIGN-BASED APPROACH TO THE STUDY OF MIND

All this suggests that a sensible approach to the study of mind,
including attempts to replicate or model mental phenomena in machines,
may be to try to isolate different sorts of capabilities, show how they
might have evolved, describe their functional roles in different animals
(and robots), explain how they are implemented in animal brains, and
investigate whether they could be implemented in machines of various
kinds. This is obviously a multi-disciplinary research programme,
requiring collaboration between biologists, brain scientists,
psychologists, AI researchers and philosophers (like myself!).

In attempting to model or replicate the phenomena, we should not
necessarily restrict ourselves to any particular form of engineering
technique: that would be silly. So, if necessary, we should combine
digital computers, neural nets,  various kinds of software engineering
techniques, chemical mechanisms, and whatever else proves useful or
necessary, including quantum gravity computers if such things are ever
created. If powerful new information processing engines become available
AI research of the future may look very different from AI research of
the last fifty years. But that does not mean that we have to wait for
such new developments doing nothing in the meantime.

On the contrary, we can try to understand what the problems are, and see
how far we can go towards solving them using mechanisms that we already
understand. If we discover limitations in those mechanisms as a result
of such efforts we shall be in a much better position to understand
requirements for new mechanisms. My own view is that so far there is no
clear evidence that new advances in physics are required for
understanding or replicating typical mental phenomena.

A fruitful framework for conducting this research is to explore "design
space" and "niche space" and their relationships.

"Design space" is the space of possible designs for systems, with
different combinations of capabilities. "Niche space" is the space of
possible combinations of requirements for behaving systems. It includes
both what biologists study when they talk about the niche of an organism
and partly what engineers study when they talk about the requirements
against which a design is to be evaluated.

Niche space and design space are both very complex structures, and the
mappings between them are very complex. What are the dynamics of these
spaces: what sorts of trajectories are possible within them? Which sorts
can occur in individual development? (As far as I know, it is not
physically or chemically possible for a human egg to evolve into a
giraffe, no matter how its environment is manipulated.) Which sorts of
trajectories require evolution across generations involving many
individuals? Which can occur in a laboratory?

Evolution also produces designs -- even though there is no designer or
engineer, just natural selection. The notion of a design is an
abstraction that has nothing to do with how the design was produced, or
whether any agent intends it to serve any purposes. In that sense the
design of a bee fits the requirement to pollinate plants. Similarly
evolution produces niches: the design of one organism may form part
of the niche for another. Thus the dynamics of niches and designs will
be intimately related.

One aspect of design that is familiar to engineers is the notion of an
{\em architecture} within which various sorts of mechanisms (and
sub-architectures) can be combined into an integrated functioning whole.
This is not necessarily a physical architecture like the architecture of
a building. For example a complex factory control system, or financial
management system, or international information network, may have a
software architecture, defined in terms of collections of interacting
modules with different sorts of capabilities, and may include several
different layers of abstraction, such as the different levels of
transmission protocols on networks.

Another important point which is not familiar to all engineers is that
the processes within an architecture need not all be describable in
terms of a fixed collection of numerical measures, such as voltage,
current, torque, velocity, pressure, etc. In an information processing
architecture many of the important changes are changes in structures
that record information, such as sentences, logical formulae, decision
trees, networks of linked structures, and so on. The changes need not
all be changes in values of numerical variables, but can include all
sorts of other changes, including the sorts of changes that we are now
familiar with in databases, game playing programs, compilers, operating
systems, word processors and the increasingly fashionable "software
agents". Further, in such systems the architecture need not be static:
software systems can dynamically reconfigure themselves to meet
different demands, and they can learn how to do this on the basis of
records of what works well in different contexts.

Thus we can conceive of a space of possible designs for information
based control systems. My conjecture is that the mind is such a control
system [sloman93], or rather there is a class of possible minds,
including minds of young children, of normal adults, or brain damaged
individuals, and also many other animals, including chimps, monkeys,
cats, dogs, nest building birds, and perhaps insects and single celled
organisms. This notion of "mind" does not have any sharp boundaries, and
that does not matter, for it's the divisions and discontinuities
{\em within} the space that are interesting, and which we need to
understand and explain, including explaining how processes of evolution
or individual development can produce transitions across those design
discontinuities.

Understanding the characteristically human control system requires
investigation of a complex architecture composed of layers with
different evolutionary histories. I shall describe three different
layers of architecture and then relate them to certain human emotional
phenomena.


A. REACTIVE ARCHITECTURES

One kind of control architecture is purely reactive. Information is
acquired through external sensors and internal monitors and propagates
through and around the system, and out to effectors of various kinds.
Everything happens automatically and in parallel because there are
dedicated, coexisting circuits, some continuous and some digital,
implementing condition-action rules, with various kinds of feedback.
Such a system can react quickly because all processing is done in
parallel. Competitions between sub-systems can be resolved either by
weighted additive mechanisms where the appropriate behaviour is a
combination of sub-behaviours, or by winner-take-all circuits where
sub-systems tend to produce incompatible behaviours.

This sort of architecture can work very well provided that the
environment for which it has been developed (by evolution, or by an
explicit designer) does not change much. If there is a lot of
variability in the environment it may be that the survival rate for
individuals is not very high, but that may not matter for the gene pool
if very large numbers of individuals can be produced cheaply and enough
survive in each generation.

I suspect that some animals are {\em entirely} like that (e.g. insects),
some are {\em largely} like that (e.g. other animals whose behaviour is
mostly genetically determined), and chimps and humans are {\em partly}
like that, with additional architectural layers providing additional
flexibility.

B. DELIBERATIVE ARCHITECTURES.

In a reactive architecture, everything happens automatically: whenever
the conditions for an action are satisfied the action is triggered,
though its effects may be suppressed by other parts of the system.

By contrast, a deliberative architecture allows the explicit
construction of alternative possible future actions, including novel
actions, along with processes of evaluation and comparison, leading to
the selection of one option, as a plan (possibly a partial plan). This
requires use of stored knowledge about the environment, about the
agent's capabilities, about preconditions and effects of actions. In
general the process of finding a suitable plan, or solution to a problem
requires a search in a space of possible structures. This can include
symbolic trial and error searching, which may be cheaper, quicker, and
safer than trying out options in the real world.

This sort of architecture requires a re-usable workspace within which
the temporary structures representing possible actions can be built and
evaluated, until a selection is made. The same workspace can then be
used for another problem. The use of such a workspace is therefore
inherently serial, even if it is implemented using parallel mechanisms,
such as neural nets. The global store of knowledge required for
constructing novel solutions may also be implemented using a highly
parallel mechanism, but that does not mean that it can answer lots of
questions in parallel: it too may be restricted to a serial mode of
functioning.

For these and other reasons that are too complicated to go into here,
the overall functioning of a deliberative architecture in which novel
temporary structures are built and selected will be inherently resource
limited. Only a limited number of things can occur at a time and the
system cannot be made faster by multiplying workspaces, whereas in a
reactive system additional parallel circuits dedicated to performing
specific tasks (e.g. visual sensing, auditory sensing, touch sensing,
posture control, control of digestion, etc.) can be added to the
architecture if needed.

In a hybrid system combining both architectures, which I conjecture is
to be found in many animals, it may be possible for useful new
structures to be moved or copied from the temporary workspace in the
deliberative architecture into dedicated reactive circuits. This seems
to be a common feature of training of athletes, training of musical
performers, learning to drive a car, and many forms of intellectual
training which require rapid recognition of structures and retrieval of
solutions to problems, as in mathematical expertise and learning to
read.

I suspect that insects do not include a deliberative mechanism (though I
am not an expert on insects). I am sure that some other animals do (e.g.
chimpanzees and bonobos) though I don't know how far they compare with
human deliberative capabilities. Detailed investigation may show wide
variations in the deliberative capabilities of different sorts of
animals, related to their different capabilities to perceive the
environment, store abstract representations for future use, manipulate
representations to solve problems, and so on.

A great deal of work in AI has been concerned with the study of
deliberative mechanisms, though there has also been a lot of interest in
neural nets and condition-action systems which can be used for reactive
systems (sometimes referred to as "behaviour-based" systems).

However, there has not been quite so much AI work on multi-purpose
deliberative architectures embedded in a complex and changing
environment with real-time constraints, although many software
engineering projects need to meet those requirements. In such contexts,
the use of the workspaces must be interruptable (e.g. because of the
need to rapidly drop whatever you are doing and thinking about in order
to decide how to escape from a fast approaching serious danger, when the
answer isn't sufficiently obvious to be settled by the reactive system).

To cut a long story short, these (empirical and engineering design)
arguments about deliberative mechanisms lead to a theory about the need
for both (a) mechanisms that can interrupt and reorganise what's going
on in the reusable workspace and also (b) filtering or "gating"
mechanisms, so that what is able to interrupt and reorganise depends on
some comparison of importance and urgency both of the existing tasks and
the potential interrupter.

We have here the seeds of an idea that sometimes these mechanisms can
"go wrong", producing too many interrupts and diversions so that
important and urgent tasks are not successfully completed. Many other
kinds of malfunctions are possible, including distorted evaluations of
the relative merits of different options, failure to use available
knowledge in constructing new solutions, unintended modifications of
complex plans and other structures so that they no longer work properly,
and many more. This is one of many ways in which the AI approach to
designing intelligent systems and work on psychotherapy and counselling
could interact fruitfully. (Unfortunately pressures on teachers and
researchers make it increasingly difficult to find time for new types of
interdisciplinary collaboration.)

C. THE META-MANAGEMENT LAYER

Because the re-usable workspace is a powerful resource that has to be
shared between different competing goals and needs which in some
environments can change rapidly, issues arise about how to schedule its
use when there are competing demands, for instance when conflicts arise
between desires to eat, to avoid an impending danger, to get warmer, to
find a mate, to protect a helpless infant, to investigate a puzzling
phenomenon, and so on.

With colleagues at Birmingham, and especially a former PhD student, Luc
Beaudoin, I have been exploring the notion of a third level of
architecture, which, in evolutionary terms, would be even more recent,
and possibly a lot more rare than the reactive and deliberative layers.
[Beaudoin94]

This involves a "meta-management" system which can (to some extent)
monitor the strategies and behaviour of the deliberative and reactive
systems (and possibly also the meta-management system itself) and take
corrective action when the individual decisions do not seem to be
producing an overall state that is valued highly.

Meta-management mechanisms could simply be a specialised subset of the
deliberative mechanism with the ability to monitor and categorise
and act upon the states of the deliberative mechanism. Thus it might
discover and remedy weaknesses in the deliberative strategies.

An example might be learning that in some cases problems can be detected
early in the process of plan formation rather than later in the
processing of running a plan. Many programmers develop their programming
capabilities in this way, but it seems to be a far more general
phenomenon.

Another example many people will find familiar is noticing that one is
switching attention between tasks too frequently, with consequent loss
of efficiency. Corrective action might involve deciding to ignore
interrupts and new motives for a time. Such control at the
meta-management level is not perfect: we sometimes decide, and want, to
think about X, but are continually drawn back to thinking about Y,
perhaps because filters in the interrupt mechanisms mentioned above are
not using optimal strategies, or because something has gone wrong
through brain damage, chemical imbalances, or whatever.

Some characteristically human emotional states seem to involve such
partial lack of control of attention. I call these perturbances, below.

The third level explains the fact that you can not only look at things
around you and do things, but you can also attend to what you are
looking at, think about what you are paying attention to, and notice
aspects of your experience, such as how what you see varies as you move,
without which much art would be impossible.

I conjecture that this third architectural layer (which, like the other
two, we still only partially understand) is to be found in humans but
probably not at birth: i.e. it seems to develop within an individual,
though the mechanisms that make it possible, and its general form, may
be genetically determined.

E.g. a very young child may be unaware that its parent has successfully
diverted its attention away from some desirable object, and therefore
the child will not redirect attention to that object. A few months or
years later the trick no longer works and parents have to be far more
subtle in their control strategies, because the child can remember that
it had an important unfulfilled goal a short time ago.

The capabilities of meta-management can be enormously enhanced through
the development of a culture that includes a language for talking about
mental states, thereby ensuring that it is not necessary for each
individual to learn entirely unaided how to categorise and control
his/her deliberative processes.

In some other mammals (bonobos? monkeys?) the meta-management
architecture may also be present, though perhaps with reduced
functionality, partly because of the lack of a human-like language.


TOWARDS A FUNCTIONAL THEORY OF `QUALIA'

Many scientists and philosophers have been puzzled by what is variously
referred to as the contents of subjective experience, qualia, what it is
like to be something. This is supposed to be specially "hard" for
science to explain, and some have argued that only new kinds of physics
(e.g. quantum gravity theory) can explain such things, while others have
claimed that they can already identify physiological mechanisms in the
brain that are a sufficient explanation. There are also debates about
whether these phenomena can occur in computer based robots or some other
kind of intelligent future machine. My own view is that there is a
mixture of philosophical confusion and scientific naivety in much of
this discussion but that insofar as there is a well defined problem the
answer can be provided within the framework of the design standpoint
illustrated by this paper.


In particular, we can consider designs in which meta-management
processes are able, in some circumstances, to inspect some of the
internal states of other mechanisms, including some of the intermediate
states of sensory processing sub-systems (which can themselves have very
complex architectures including several layers of interpretation
mechanisms using more and more abstract virtual machine structures), and
some of the states of the deliberative and control systems. The
inspection may always be only partial, and may always involve an element
of interpretation or translation (e.g. from a low level sensory notation
to a formalism that can be used by the meta-management system). But
there is nothing mysterious about it. Self-monitoring, though much
simpler in form, is commonplace in computer operating systems and it may
turn out that extensions of those techniques will suffice for
self-monitoring in more intelligent machines.

This internal monitoring of intermediate sensory stores happens for
example, when you learn to see that besides the rectangular shape of the
table in front of you there is also a skewed shape (a parallelogram)
which is not out there but is part of an intermediate representation
within you, determined by the structure of the viewpoint relative optic
array: i.e. it changes shape as you move. Without this internal
monitoring capability, "realistic" 2-D painting or drawing of 3-D
objects would be impossible.

The biological functions of the "qualia" are real, but too complex to
describe in detail here. E.g. they can play a role both in individual
learning, and also in communications where one agent tries to help
another "debug" an internal process:

    "Look just a teeny bit to the left of the large green triangle,
    dear.... then you'll see it"

where there is no green triangle out there, but from the current
viewpoint there's a triangular region caused by e.g. the way two sloping
walls and a floor bound a visible part of a rectangular lawn).

On this model there are many different kinds of qualia (contents of
self-monitoring states), and they are to be explained at a functional
level in terms of the architecture that makes them possible, and at a
physical or physiological level in terms of the mechanisms used to
implement the architecture, which may be different in different
organisms or machines.

The three layers in the proposed architecture, the automatic reactive
processing layer, the management layer which can construct and
contemplate options in advance, and the metamanagement layer which can
monitor, evaluate, and redirect high level internal actions, may each
add new kinds of capabilities compared with earlier, simpler systems.
Within the space of possible designs we can expect to see many
discontinuities, depending on which mechanisms and which functions are
present.

Trying to treat all cases as part of a single (smooth) continuum is
therefore seriously misleading, as it diverts attention from the
important task of identifying the discontinuities and the mechanisms
underlying them.

WHAT DOES ALL THIS HAVE TO DO WITH EMOTIONS?

There are some aspects of emotion that we share with many other animals
and depend on processes in the reactive subsystems of the brain. These
include being startled, terrified, thrilled, distressed by extreme pain,
disgusted by horrible tasting food, sexually aroused, and no doubt many
more. I am not an expert on brain processes but I understand that the
limbic system plays a major role in these processes. I suspect that when
we have a more fine grained understanding of the functionality of the
reactive architecture and the way it interacts with deliberative and
meta-management mechanisms we shall be able to produce a more systematic
taxonomy of control states including emotions, moods, desires and
various kinds of pleasures and pains.

However, there are some sorts of emotional states that (it seems) cannot
occur in rats or cats, but are commonplace in human beings, and may
require something other than a purely reactive architecture. These are
the emotions that poets, novelists, playwrights and gossips are
interested in. Examples are being humiliated in front of a rival,
ashamed of what one has done to a friend, awe-struck by a magnificent
building, thrilled at winning a gold medal, or obsessed with a desire
for revenge or promotion? In [Wright96] the example of grief is analysed
in some depth and related to the sort of three-layered architecture
described here.

Months after a road accident that killed his child a grieving father may
find it difficult to avoid thoughts about the child, what might have
been done to avoid the accident, what the child would have been doing
now, what should be done to the driver of the car, and so on, even
though he wishes not to wallow in these thoughts and even though these
thoughts interfere with other activities that he values highly and
wishes to concentrate on (e.g. preparing a report or doing something for
another member of the family). One feature of these characteristically
human types of emotional states is that there is a partial loss of
control of thought processes. This makes sense only in the context where
one can sometimes be in control. I call these states perturbant.

My claim is that not all information processing architectures support
such a context. For example, the purely reactive architecture does not.
However, where there is a deliberative layer and a meta-management layer
in the architecture it is possible to have "meta-management" goals
regarding how the (resource limited) deliberative layer is deployed and
to find that those goals are consistently being violated by mechanisms
that grab and divert attention away from the chosen tasks.

To summarise: sometimes we control thoughts e.g. deciding to cease
dwelling on yesterday's humiliating experience and instead pay attention
to an important task. But sometimes we fail. So we also have the ability
to lose control of our thought processes, at least partly.

It might be thought that this is a kind of malfunction which is due to a
design flaw, and that a different sort of design could prevent such
states occurring. My conjecture is that this is not so. There will have
to be mechanisms that can sometimes override high level decisions,
because deliberative and meta-management mechanisms are inherently
resource limited so that various sorts of sub-processes will need to
compete for attention, and it is not safe to suppress all interrupts by
always allowing high level decisions to dominate processing. These
interrupting mechanisms, however, will sometimes have to act quickly and
on the basis of partial information, so they will have the potential for
getting things wrong, and causing perturbant states that override top
level decisions. It may be possible to suppress these mechanisms in
special cases: and that may be what has happened in people whom we
regard as completely cold and calculating.


CONCLUSION

The human brain is perhaps the most complex system ever studied by
science and it is not to be expected that we shall achieve a full
understanding in the foreseeable future.

If we aim for an architecture-based understanding, we can get a much
deeper understanding of what sort of control system a human mind is, and
how different minds, human and non-human, normal and abnormal, infant
and adult, human and machine, are similar, and how they differ. Within
this framework we can generate architecture-based concepts for
describing classes of mental states and processes that are possible in
different architectures, just as the theory of the architecture of
physical matter generated a new system of concepts for classifying kinds
of stuff, the periodic table of the elements. We still await a periodic
table for human minds.

Within this design-based approach, enhanced by neurophysiological
studies of the underlying mechanisms, we can hope to find deeper
theories of how a human mind normally works and also how it might go
wrong: the more complex the architecture, the more ways it can go wrong.
By applying all these ideas, we should be able to help therapy,
counselling and education.  Most educational theories are not based on a
deep understanding of what kind of mechanism we are talking about, just
a lot of hunches and rules of thumb (some of which work very well).

So within the framework of AI, in collaboration with other disciplines,
we can hope for new insights into matters that concern us all. As yet
there is no evidence that this requires a new extension to quantum
physics as Penrose claims. Even if we already had such a theory, there
is no evidence that it would help us understand the design issues
described above any better.

=======================================================================

                         REFERENCES

[Beaudoin94]
L.P. Beaudoin, Goal processing in autonomous agents,
PhD Thesis,
School of Computer Science, The University of Birmingham,
1994, available in three formats in the following compressed files
in the above ftp directory:

    Luc.Beaudoin_thesis.ps.Z    (postscript)

    Luc.Beaudoin_thesis.rtf.Z   (rtf)

    Luc.Beaudoin_thesis.txt.Z   (plain text, without diagrams)

[Sloman92]
A. Sloman, `The emperor's real mind: review of Roger Penrose's
{\em The Emperor's new Mind: Concerning Computers Minds and the Laws of
Physics,'} in {\em Artificial Intelligence,} 56 (1992) pp 355-396


[Sloman93]
A. Sloman,
`The mind as a control system', in
{\em Philosophy and the Cognitive Sciences,}
(eds) C. Hookway and D. Peterson,
Cambridge University Press,  (1993) pp 69-110,

[Wright96]
I.P. Wright, A. Sloman and L.P. Beaudoin,
Towards a Design-Based Analysis of Emotional Episodes,
(To appear in)
{\em Philosophy Psychiatry and Psychology}, 1996.

Many of the papers produced by the Birmingham Cognition and Affect
project can be found on the internet in the project directory:

    ftp://ftp.cs.bham.ac.uk/pub/groups/cog_affect

The toolkit we are developing for exploring a variety of agent
architectures is described in this online WWW document, with some simple
demonstration "movies":

    http://www.cs.bham.ac.uk/~axs/cog_affect/sim_agent.html

[end]
