This is a provisional abstract for a talk to be presented (with some
videos showing infants and toddlers functioning concurrently at
different levels of abstraction, if time permits) at
The 8th
Understanding Complex Systems Symposium,
UIUC, May 12-15 2008.
According to the Symposium Schedule the talk is scheduled for 11am Tuesday 13th May.
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I shall discuss a collection of competences of humans and other animals that appear to develop over time under the control of both the environment and successive layers of learning capabilities that build on previously learned capabilities.For example after an infant has learnt to control some of her movements, she can use that competence to perform experiments on both the physical environment and nearby older humans.Such competences are usually studied separately, in different disciplines.After new forms of representation have been learnt they can be used to learn how to form and test new plans, goals, hypotheses, etc.
After new concepts have been acquired they can be used to formulate new theories.
All of the above can help drive the development of linguistic and other communicative competences.
Being able to communicate with others makes it possible to learn things that others have already learnt.
These layered learning processes start in infancy, and, in humans, can continue throughout adult life.
Likewise people who attempt to build working AI models or robots consider only a small subset of the competences shown by humans and other animals, and different researchers focus on different subsets.
It is not obvious that models or explanations that work for a narrowly focused set of tasks can be extended to form part of a more general system: systems that "scale up" do not always "scale out".
Analysis of combinations of different sorts of competence, including perceiving, reasoning, planning, controlling actions, developing new ontologies, playing, exploring, seeing explanations, and interacting socially, provides very demanding requirements to be met by:
Both tasks need to take account of the distinctive features of 3-D environments in which objects of very varied structure, made of many different kinds of materials with different properties, can interact, including objects manipulated by humans, animals or robots.
- human-like robots that develop through interacting with a rich and complex 3-D world
- an explanatory theory of how humans (and similar animals) do what they do.
That manipulation includes assembling and disassembling structures of varying complexity, and varying modes of composition -- including food!
The evolutionary niches associated with such environments posed combinations of problems for our biological predecessors that need to be understood if we wish to understand the products of evolution.
Consideration of a space of niches and a space of designs for different sorts of animal and different sorts of machine reveals nature/nurture tradeoffs, and indicates hard problems that AI researchers, psychologists and neuroscientists have not addressed, e.g. why a robot or animal that learns through play and exploration in a complex, changing 3-D environment needs competences (e.g. the ability to perceive and reason about both action affordances and epistemic affordances) that also seem to underlie human abilities to do mathematics (including geometry and topology).
Viewing mathematical competence as a side-effect of evolutionary processes meeting biological needs can shed new light both on old philosophical problems about the nature of mathematical knowledge and on problems in developmental psychology and education, especially mathematical education.
I shall talk briefly about the kind of self-extending, multi-functional, virtual machine information-processing architecture required to explain such human capabilities; a requirement that no current AI systems or current neural theories come close to addressing.
The hypothesized required architecture consists of a very complex dynamical system
- composed of a network of dynamical systems of different sorts
- that grows itself,
- which can be contrasted with the very much simpler kinds of dynamical system that have so far been investigated in biologically inspired robotics.
The two diagrams below illustrate in a sketchy fashion, both the simple systems and the kind of complexity that we require, and which I suggest exists in virtual machines that run on human brains and the brains of some other animals, and will need to be replicated in human-like robots.
Many people who have grown disillusioned with symbolic AI mechanisms fail to realise that something like those mechanisms are needed for the more abstract, more loosely coupled dynamical systems, for example the ones that enable you to do algebra in your head, discuss philosophy, make plans for conference travel, or read these notes.
Likewise, people who think symbolic AI will suffice for everything fail to attend to the kinds of intelligence required for controlling continuous actions in a 3-D structured environment, including maintaining balance, drawing a picture with a pencil, and playing a violin. Many such activities require both sorts of mechanism operating concurrently.
Sometimes the fact that humans, other animals, and robots are embodied can lead thinkers to make the mistake of assuming that only the first type of dynamical system is required because they forget that some embodied systems (including those people) can think about past, future, distant places, games of chess, transfinite ordinals and how to design intelligent systems.
The different roles of dynamical systems operating concurrently at different levels of abstraction need to be accommodated in any theory of motivation and affect, since motives, evaluations, preferences, and the like can exist at many levels.
For a critique of some shallow theories of emotion see
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#cafe04
"Do machines, natural or artificial, really need emotions?" (PDF presentation).and
http://www.cs.bham.ac.uk/research/projects/cogaff/04.html#200403
"What are emotion theories about?" (PDF -- paper for AAAI Spring Symposium 2004).
We can crudely decompose the variety of sub-processes that occur in biological organisms in two dimensions
- Whether perceptual/sensory, or central or concerned with effectors/actions.
- Whether based on evolutionarily old reactive mechanisms, or deliberative mechanisms or meta-management mechanisms (concerned with self-monitoring or control, or using meta-semantic competences in relation to other agents).
That produces a 3by3 grid of types of sub-system as illustrated below.
(Note that 'reactive' here does not imply closely coupled with the environment).
The CogAff Architecture Schema
The grid is only an approximation -- more subdivisions are to be found in nature than this suggests (in both dimensions, but especially the vertical dimension).
(N.B. Biological evolution can produce only discrete changes, not continuous changes. The discrete changes vary in size and significance: e.g. duplication is often more dramatic than modification, and can be the start of a major new development.)
The H-CogAff (Human-CogAff) Architecture Sketch
There will not be time to explain all of this, but there is lots more here:
There is a space of possible designs for working systems (design space) and a space of possible niches within which systems can function, learn, evolve, etc. (niche space). There are complex mappings between those spaces, with no simple one to one relationship.
It is not helpful to think of the relationship between a design and a niche as numerical fitness value, nor even vector of fitness values in different dimensions. Rather (as in consumer reports), there will be structured relationships between features of designs and features of niches, e.g. specifying what the consequences are of a particular design limitation in a particular class of niches.
Compare the type of analysis of bugs in a program that helps a good programmer think about how to improve the program.
Since the niche of a particular organism depends in part on the design features of a set of other organisms (helping to determine their physical characteristics and their behaviours, learning abilities, etc.) we can say that the niche inhabited by instances of a particular design is partly produced by other coexisting designs.That niche will tend to produce evolutionary pressures on the designs instantiated in it. So designs will change, and there are consequently evolutionary trajectories through design space.
However, since changes in the designs will lead to changes in the niches they produce there will also be evolutionary trajectories through niche space (e-trajectories).
The individuals that are instances of evolving designs will undergo learning and development, producing individual trajectories (i-trajectories).
At every stage the designs and individuals must be biologically viable. Contrast that with the case of a human tinkering with a design: during some of the discontinuous changes there may be no working instances. The corresponding trajectories (which may be called repair trajectories (r-trajectories) will be discontinuous, unlike i-trajectories and e-trajectories.
Insofar as the individual members of a design or class of designs are able to communicate, and acquire information that is passed on to off-spring, we can talk about cultures being added to the virtual machinery. The will then also be cultural or social trajectories in the spaces (not depicted in the diagram).
The concurrent evolution of both multiple designs and multiple (often co-located) niches forms an enormously complex dynamical system, which can be thought of as exploring several spaces of possibilities concurrently.
An ecosystem, or even the whole biosphere, can be thought of as a complex virtual machine with multiple concurrent threads, producing multiple feedback loops of many kinds (including not just scalar feedback but structured feedback -- e.g more like a sentence than a force or voltage).The individual organisms, insofar as they implement some of the designs indicated above, will also include multiple virtual machines.
I assume all these virtual machines are ultimately implemented in physics and chemistry, though there are some people who don't like that idea. So far we don't know enough about what variety of virtual machines can be interested on the basis of physical principles as we currently understand them.
If it turns out that there is something that cannot be implemented in that sort of physics because something more is required this is more likely to support the conclusion that our physical theories need to be extended rather than the conclusion that physics is not enough.
