School of Computer Science


I stopped trying to obtain funding for this work several years ago.
It wasted too much time, and even if funding becomes available,
finding suitably educated researchers with the right sorts of
broad interests and willingness to take risks with their own
careers is too difficult. So I just get on with it, and anyone
interested can join in.

Since 2011, the work has been increasingly dominated by the
Turing-inspired Meta-Morphogenesis project
A large component of this is attempting to understand how
biological evolution (natural selection) functions as a
"blind mathematician" that is able to produce increasingly
less blind mathematicians.



The main goal of the CogAff project was to understand the types of architectures that are capable of accounting for the whole range of human (and non-human) mental states and processes, including not only intelligent capabilities, such as the ability to learn to find your way in an unfamiliar town and the ability to think about infinite sets, but also moods, emotions, desires, and the like. For instance, we have investigated whether the ability to have emotional states is an accident of animal evolution or an inevitable consequence of design requirements and constraints, for instance in resource-limited intelligent robots.

We also hoped to show that many of our current mental concepts (including "emotion", "consciousness" and many others) are inherently ambiguous "cluster concepts" which can be refined and clarified by deriving more precise and richer families of concepts from specifications of architectures able to support human like mental states and processes.

However, it is not possible to understand one type of mind fully without understanding how it is similar to and how it differs from others, and what the implications of those similarities and differences are. So the study of human minds has to be part of a larger investigation, including various kinds of animals, possible robots, and even minds which might in principle have evolved but did not.

An example of the "architecture based" approach is the realisation that there are at least three very different kinds of emotions related to different architectural layers described below. In particular there are primary and secondary emotions shared to varying degrees with other animals and tertiary emotions which use an architectural layer that perhaps is found only in (adult) humans, and perhaps very few other animals (orangutans, chimpanzees and bonobos perhaps? Probably other animals too.)

The work also has implications for theories of different kinds of consciousness, the nature of vision, forms of representation, varieties of learning and development, and possible evolutionary trajectories. In particular, we attempt to understand the trade-offs which led to the evolution of hybrid multi-layer information processing architectures implemented in human brains.


Our study of design principles for intelligent autonomous agents, whether natural or artificial, includes the following topics:

  1. The ontology of a human-like mind: what sorts of states, properties, processes, capabilities can occur in various sorts of minds, e.g. beliefs, desires, decisions, deliberation, intentions, plans, suppositions, idle wishes, preferences, ambitions, motive generators, personalities, emotions, moods, loss of control of attention, and states and processes possible only in minds that are unlike ours.
  2. What kinds of architectures can support biological and artificial agents with different kinds of intelligence?
    This requires a study of `design space' and `niche space' and their relationships, including the different sorts of trajectories possible in these spaces, e.g. for an individual, for a naturally evolving species, or for a system explicitly modified or repaired by an engineer.
  3. To what extent do humans and other agents have simple and uniform architectures, and to what extent do they have hybrid architectures, e.g. combining neural nets interacting with symbolic reasoning systems? Is a human brain an unintelligibly complex morass of mechanisms, or is there sufficient modularity of design to enable us to attain at least a partial understanding of how we work?
  4. How do various kinds of mental states in humans (and presumably other intelligent agents) arise out of the architecture (e.g. emotional states)? What is the relationship between the philosopher's notion of a mind SUPERVENING ON a body and the engineer's notion of a virtual machine being IMPLEMENTED IN a physical machine?
  5. What forms of motivation are there (desires, wishes, pleasures, dislikes, etc.), how they are generated, and how they are managed in autonomous agents. What sorts of motivation can be generated within different sorts of architectures? Is there a distinction between motives that come from an individual and those which are produced entirely by "external" causes?
  6. What kinds of learning and development are possible in agents with different sorts of architectures? This includes processes such as including acquiring new facts, new rules for internal or external behaviour, new forms of representation, new links between components of the architecture, and adding new sub-systems to the architecture. (A newborn infant doesn't have the same architecture as an adult.)
  7. How can we design interacting communicating agents? How are the possibilities for communication, cooperation, competition and other interactions BETWEEN agents related to the architectures WITHIN those agents?
  8. How can resource-limited agents cope with time pressures and limited knowledge in their deliberations? If it is hard to get the design of such a deliberative mechanism optimised for all situations, would it be useful to have a higher order meta-management mechanism able to observe, evaluate and modify the deliberative mechanisms?
  9. Can we evolve artificial human-like architectures using genetic algorithms and genetic programming, or similar techniques? What are the requirements for such evolution to succeed within a reasonable amount of time given the astronomical size of the search space in which complex designs are embedded? Is such evolution truly a matter of random change with selection-driven hill-climbing or are there more subtle knowledge-based control mechanisms implicit in some of the mechanisms (especially when co-evolution is involved)?
  10. Will the study of natural evolution and the study of artificial evolution be mutually informative?

These questions and some sketchy answers are discussed in more detail in our research reports and seminar presentations in the project paper directory. There is also a directory with miscellaneous discussion notes and draft or wild papers:


We have conjectured that human-like architectures require several different sorts of concurrently acting sub-architectures to coexist and collaborate including a "reactive" layer, a "deliberative" layer and a "meta-management" layer, along with one or more global "alarm mechanisms", a long term associative store (e.g. for answering "what if?" questions), various motive generating mechanisms, and layered perception and action mechanisms operating at different levels of abstraction.

The different components of the architecture will have evolved at different times under the influence of different sorts of evolutionary pressures and will be subject to different sorts of constraints and tradeoffs. E.g. the global alarm mechanism may have to sacrifice accuracy and correctness for speed. This may be why many emotional reactions are inappropriate.


Like the OZ project of Bates and colleagues at CMU (see below), we aim to start with "broad but shallow" architectures. That is, the architectures should accommodate and integrate a wide range of functions, such as vision and other forms of perception, various kinds of action, motivation, various kinds of learning, skilled "automatic" behaviour, explicitly planned behaviour, various kinds of problem solving, planning, self-awareness, self-criticism, changing moods, etc.

A "broad" architecture contrasts with "deep and narrow" systems, like most AI systems, e.g. systems to analyse images, or understand sentences, or solve mathematical problems, or make plans, etc.

It may be necessary for a while to tolerate relatively shallow and simplified components as we explore the problems of putting lots of different components together. Later we can gradually add depth and realism to the systems we build. Shallowness is not an end in itself.


It is to be expected in this context that many aspects of human minds will not be a product of explicit mechanisms which evolved to produce those capabilities, but will "emerge" as side-effects of the interaction of many mechanisms whose primary function is different. For example, we hope to show that certain motivational and emotional states and processes for which other researchers postulate explicit rules, can instead emerge from deeper and more general mechanisms in resource-limited agents. Thus the ability to feel humiliation is not the product of a humiliation module or any specific emotion module, but a side-effect of the interaction between many different modules.

Software Tools (SimAgent and Poplog)

We are developing and using a flexible toolkit described here: This is designed so as to make it easy to explore alternative architectures for individual or interacting agents.

The toolkit is based on Poplog Pop-11. Poplog is available free of charge with full system sources. It runs on a variety of platforms.

The Leverhulme-funded project

The Leverhulme Trust provided funds for a project, to investigate Evolvable virtual information processing architectures for human-like minds. For further information on the project see

Others involved in the more general Cognition and Affect project are listed here


There is an email list for discussions of cognition and affect (e.g. architectures of autonomous agents, and processes involving motivation and emotions). If you wish to join, send a message addressed to containing just one line
    subscribe cognition_affect

After that you can send a message to the list itself giving information about yourself - who you are, what you do, where you are, and your email address.

Please do not advertise the list to other list managers: we are all bombarded with too many irrelevant announcements.

For information about closely related work at Carnegie Mellon University see The OZ project at CMU, which includes a link to the ftp directory for OZ papers. There are also some papers by Scott Reilly

For work on Interface Agents here at the University of Birmingham, see: Andy Wood's Interface Agents file which also provides pointers to related work at other sites.


Here is a list of pointers to material relevant to cognitive science and AI.

Some media links to our work.

General information about the School of Computer Science.
General information about the University of Birmingham.

This file is maintained by Aaron Sloman, and designed to be lynx-friendly, and viewable with any browser.
Last updated 22 Feb 2009 (just tidied up some links)
No longer maintained, really.
Email A.Sloman AT