A partial index of discussion notes is in http://www.cs.bham.ac.uk/research/projects/cogaff/misc/AREADME.html
There is no point attempting to define a sharp boundary physical or chemical control and information-based control, though the the more indirect and context sensitive the connection between a cause and its effect the more likely it is that the cause provides information as opposed to a force, or physical trigger, or an energy source.
In one sort of case complex collections of chemical interactions occur in which molecules are formed and then decomposed under the influence of catalytic reactions and external sources of energy (e.g. geothermal heat). In other cases, some structures capture additional molecules and go on doing so until they can divide into two structures that can repeat the processes of accretion and division into either two replicas of the original structure or at least can spawn an offshoot with the ability to continue the accretion until the original is copied with the copy able to do its own accretion and spawning. In all of these cases there two very different kinds of causal factors at work namely (a) existence of structural correspondences between complex molecules that make chemical combination possible and (b) spatial juxtaposition that allows the chemical potential to be fulfilled. So the reproductive process requires both growth of appropriate chemical structures and spatial reorganisation to align portions of those structures.
As organisms became larger, with articulated bodies capable of producing motion or manipulating other objects, and with internal and external sensors providing information about states and events both inside the organism and in the environment, the structures being controlled became larger, including mouths, fins, articulated limbs, grippers, wings, etc. In all these cases, the processes that are controlled include continuous (or partly continuous) motions, such as translations, rotations, contraction, stretching, flapping, and coordinated motion of two or more limbs.
Processes in the controlling mechanisms are very different from the processes controlled. Often the mechanisms are very much smaller, and undergo much more rapid changes. Insofar as they involve chemical mechanisms the controlling processes will be discrete, even if the processes they control are mostly continuous. (I'll return to the continuous/discrete contrast later.)
2.1 Rigid vs flexible control
Engineers have known for a long time that there are tradeoffs
between physical design and software
control requirements, and that compliance, e.g. in a wrist, often
reduces the control problems. Such
design principles are also evident in many biologically evolved
systems. Taking this to an extreme,
it is sometimes suggested that no internal control is required, as
demonstrated by passive walking
robots that "walk" down a slope under the influence of gravity,
waddling from side to side.
For an example see this video:
http://www.youtube.com/watch?v=N64KOQkbyiI
But this sort of argument can be compared with arguing that a marble demonstrates intelligence when released at the top of a helter-skelter, since it finds its way to the bottom. The marble is constantly controlled by its own momentum, gravity, and the forces applied to it by physical surfaces that it comes into contact with, and the same is true of the robot. In both cases the competences are very limited. The passive walker robot cannot walk up a hill, follow a spiral slope, or detect a brick in its path and take action to avoid falling, to name but a few of the limitations that would not affect a suitably designed, or trained, information-based walker, and which normally do not affect biological walkers. More generally, control often involves use of information to determine when and how to release internal energy to oppose other influences, such as gravity, wind, fluid currents, and forces applied by other large objects, some of them other animals, such as predators. Information about both the environment and current goals or needs is required for the appropriate movement options to be selected and used.
Sometimes opposition to an external force requires no special control process, for example, a small pebble rolling down a slope hits the foot of an animal and stops rolling, just as if it had hit a larger stone or a tree trunk. In other cases, opposition to external forces is based on external sources of information and is more effective than physical opposition: for example, detecting a large rock rolling downhill and either moving out of its way or moving another large rock into its path before it arrives can be more effective than detecting the impact when the rock arrives and passively resisting its motion: a fatal strategy in some cases. In such situations, effective strategies require internal energy resources be deployed to move out of the path of the rock, or to push an obstacle into its path, and the details of what can be done depend on information about the environment acquired in advance of and during the actions.
Common human experiences show clearly that often the most effective response to a physical situation is not based simply on immediate reactions to physical interactions, but requires use of information acquired well in advance of some critical situation, information that can be used to select and perform preparatory actions that anticipate the future, for example building a dam to meet future irrigation requirements.
In that sort of case, as in the vast majority of engineering projects, most of the details of the construction process need to be worked out well in advance, and last minute changes in height, thickness or materials of the dam wall in response to new information about rainfall, or new irrigation needs, is not possible; whereas in other cases of anticipation some of the final details can be left unspecified, until the last moment, for instance running to catch a ball while allowing the precise position of hands to be based on continuously tracking the path of the ball. Contrast that with trying to predict the exact location and pose of the body when the ball is caught, using only information available when the running starts: in general a humanly impossible task.
The use of partial, or imprecise, advance planning combined with continual adjustment (sometimes up to the lost moment, sometimes not) can be seen as an information-based strategy that has much in common with the force-based strategy using physical compliance. The former is "information-based compliant control" (IBCC), the latter "force-based compliant control" (FBCC).
Biological evolution has clearly produced both mechanisms. 2.2 Information-based vs force-based compliant control In some cases of IBCC, the processes of control can use widely applicable strategies (e.g. tracking motion while heading for a continuously adjusted interception point) so, in those cases, it is possible for a version of the IBCC strategy to be selected by biological evolution.
In other cases, the anticipatory actions need to make use of specific features of a local environment, which may be different for different members of the same species, e.g. information about spatial locations of various nutrients. E.g. some might be in a cave, some on a specific tree, some on a hillside. Knowledge of the spatial layout of the terrain might be used for route-planning when going from one of the locations to another, which could be out of sight initially. If these locations are unchanging across many generations, then the information could be absorbed into the genome like the migratory information apparently inherited by some birds. In other cases, the genome can specify only learning mechanisms for discovering where the nutrients are located and how to use the information to meet changing needs. What is specified genetically in this case could include forms of representation plus associated mechanisms, usable by individuals to construct specific (possibly map-like) stores of information about large-scale spatial structures.
(Note: Thinking about the evolution, development and learning of information-processing capabilities leads to the conclusion that the "Baldwin Effect", a postulated process by which what is first learnt is later absorbed into the genome, is just one among many forms of trade-off between species learning and individual learning.)
This sort of evolutionary transition may require a process of abstraction from a working mechanism, to produce a more schematic genetic specification that can be instantiated differently in different individuals. The best known and most spectacular example of that is the human genome's apparently unique support for processes of learning and development capable of leading thousands of significantly different human languages. But that is itself likely to be a variant of something more general found in a wider range of species, as suggested below. (Programmers with experience of a wide variety of programming languages and paradigms, and programming tasks of varying complexity will recognize that similar transitions occur in the mind a programmer, or in a programming community, over many years. These are relatively new forms of human and cultural development, which suggest ideas for evolutionary transitions that were previously unimaginable, e.g. by Darwin.)
More sophisticated organisms can use not only information about impending physical events but also intentional information-based influences coming from other organisms, e.g. threat signals, collaborative signals, invitations, sexual approaches, arguments, demonstrations, and many more. The ability to be influenced by external information has many forms ranging from very ancient and simple reactions such as blinking when detecting a rapidly approaching object to making use of complex learning and reasoning abilities. In a huge variety of cases, though not all, the information processing requires internal manipulation of information bearing structures usually implemented in microscopic physical/chemical mechanisms.
The ability of sub-microscopic chemical processes within an organism to control application of forces to much larger objects requires the ability of the smallest entities to modulate release of stored energy in larger structures, such as membranes, cells, muscles, limbs, and in some cases external machinery. Deciding what to do and when to do it, and knowing why, all require the ability to use information. However, as indicated in the CogAff architecture schema, different processing layers in the same organism, which evolved at different times can manipulate and use very different sorts of information (Sloman, 2003). An earlier version of this idea was proposed in MacLean's theory of the "triune brain" (MacLean, 1990),
The core of our problem is not how varied physical forms evolved or how varied physical behaviours became available but how ever more sophisticated information-processing capabilities evolved, making use of the physical forms, to produce the behaviour -- and what they were.
A necessary condition for the observed variety of physical forms and physical behaviours is the existence of sub-microscopic reconfigurable components (i.e. physical sub-atomic particles, atoms, molecules, etc.), capable of being combined to form a huge variety of more or less stable or multi- stable structures, on many scales. Many of the structures composed from myriad parts can move as coherent wholes through 3-D space, in some cases changing their structures (internal relationships) and their (external) relationships to other structures.
Some of the motions and other changes are produced solely by external influences, while others are self controlled. Some use only information about the immediate environment, and discard it after use (on-line intelligence), while others refer to possible future events and possible past events that could explain current facts, or to planned and unwanted events (off-line intelligence). The difference is discussed in more detail later.
Initially only physical and chemical processes were controlled, e.g. reactions to contact with nutrients or noxious substances, and motion towards or away from other occupants of the immediate environment. Later, control mechanisms were controlled, e.g. selecting between two competing control mechanisms, or creation of new control mechanisms and many other forms of information processing now found in individuals, social systems, and ecosystems.
Various sorts of (positive and negative) affordances for producing change provided by physical environments of different sorts, are ubiquitous, e.g. opportunities to collide with, avoid, push, pass through, pull, twist, bend, rotate, squeeze, tear open, or eat physical objects. As a result, it seems that overlapping information processing capabilities emerged independently in very different evolutionary lineages, namely perceptual and motor information processing required for control of widely used physical actions (e.g. in octopus, felidae (cats), parrots, squirrels, corvids, elephants, and primates. (I am using a generalisation of Gibson's notion of "affordance", as explained in (Sloman, 2011c)). Convergent evolution of cognitive competences can provide opportunities for convergent evolution of meta-cognitive competences.
In the more recent history of the planet, the growth in physical size and complexity of animals, along with increasing sophistication of control mechanisms, seems to have been accompanied, in some organisms, by continual growth of meta-cognition: understanding of what is possible in advance of action, and of what has been done, and what else might have happened, and how to select among competing cognitive and meta-cognitive capabilities (meta-management (Beaudoin, 1994), or reflective intelligence (Minsky, 2006)), including abilities to compare and reason about not only alternative possible actions, but also alternative goals, or alternative planning or reasoning strategies.6
Later on, increasingly sophisticated control processes were themselves created and controlled by other control processes. Meta-control took on increasingly intricate and varied forms, including, in some cases, use of external records and reasoning aids, and training or teaching of one individual by another, shortening times required for individuals to develop some of the more sophisticated forms of information processing. Although there have been various isolated attempts to design meta-cognitive capabilities in AI (some of them echoing aspects of Freud's "Super-ego" concept), e.g. (Sloman, 1978a; Shallice & Evans, 1978; Minsky, 1987; Newell, 1990; Russell & Wefald, 1991; Karmiloff- Smith, 1992; Beaudoin, 1994; Cox & Raja, 2007; Sloman, 2006b; Shallice & Cooper, 2011), and very many more, most of them refer only to humans, or to a specific proposed AI system. As far as I know nobody has attempted to compile a comprehensive survey of types of meta-cognition that can be useful for biological or artificial systems, and the environmental and other sources of pressure to select them.
We do not yet have an adequate vocabulary to describe all these "high level" control capabilities, though familiar words and phrases like, "sense", "perceive", "learn", "want", "imagine", "decide", "resist", "refrain from", "plan", "attend to", "think about", "understand", "communicate", "conscious of", and many more, point at some of these capabilities and processes. Recent additions, such as "cognition", "meta-cognition", "executive function", and other jargon used by philosophers and scientists, may suggest deep theoretical advances, but often merely indicate groping towards new forms of understanding about the varieties of information processing in humans and other animals.
We cannot expect good theories until we have much better ontologies for possible constituents of explanatory theories, based on deeper analysis of more varied naturally occurring control problems.
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Maintained by
Aaron Sloman
School of Computer Science
The University of Birmingham