p.1 Until now the theory of problem-solving (e.g. Newell and Simon, 1972) has mainly emphasized the search of solutions within a problem space. From this viewpoint, problem-solving capability (i.e. intelligence) should be seen as the possession of adequate heuristics, which allow to make the search more efficient. This view presupposes that the search space is explicit and well-structured, i.e. that at each decision point there is a well-defined set of operators (or problem states to be generated) from which the most promising can be chosen according to some heuristic rule.
p.1 we have learned a lot about problem-solving and about intelligence by constructing computer models of how to play games or how to manipulate toys.
However, this does not mean that we are able to build models of how to cope with the real world... in order to experiment efficiently with such systems [autonomous agents capable of directly interacting with a real environment] we should simultaneously develop a theory of problem-solving in complex, ill-structured environments.
p.2 Clearly, the first thing to be done in order to solve an ill-structured problem is to formulate it in a well-structured way... Such a well-structured formulation is traditionally called a representation of the problem.. We will now propose a conceptual framework for the analysis of representations... The goal or function of a representation is to structure the field of experience of the intelligent agent using the representation, in such a way that the agent can search efficiently for solutions when confronted with a problematic situation, which is to be changed.
p.2 The second question to be asked is then: what are its elements? The elements we are looking for are primitive structurations of the problem environment as experienced by the agent.
The simplest form or structure we can imagine is a distinction (Spencer-Brown, 1969).
p.3 The more general you want your representation to be, i.e. the more potential problems you want to be able to solve, the more distinctions you must make.
p.5 The evolution of states in the search representation can be summarized by the generate-and-test principle... This principle is equivalent to the principles of trial-and-error and variation-and-selection. In each case there is a first phase of variation... followed by a second phase of selective retention
p.6-7 It is clear then that the variety of potential distinctions to be generated is extremely large. The difficulty resides in the selection of the most adequate distinctions. Ultimately this adequacy is determined by the capability of the distinction system to ensure the survival of the agent, i.e. to ensure that the agent will not be eliminated by natural selection. Hence there is an a priori selection criterion, determined by the relation between the agent and its environment. This criterion is based on the stability, homeostasis, capability of counteracting destructive perturbations, ... , of the agent. However, this criterion can only be applied in a very indirect way to the selection of representational distinctions.
We hence need "mediating" criteria, which would connect the representational distinctions to the survival-destruction and physical stimuli distinctions. One way to define such a "vicarious" selection criterion (cfr. Campbell, 1974), is by replacing stability with respect to the physical environment by stability with respect to the representational environment of already given distinctions. A distinction could be considered stable in this sense if it would be conserved by all representational operators, functions or relations, i.e. if it would be continuously connected to the other distinctions (continuity can indeed be defined as the conservation of topological distinctions, cfr. Heylighen, 1987a).
