Page:Advanced Automation for Space Missions.djvu/65

 scientific investigation and mission survival" (the two most complex sets of tasks facing an autonomous exploratory system) and "patterns of order" to mean "the complex abstractive and conceptual structures related to scientific investigation and mission survival" (e.g., hierarchical schemes and terrain maps, respectively). Hence the working definition of advanced machine intelligence in the context of the present study may be summarized as follows: Advanced machine intelligence is the ability of a machine system to autonomously formulate and to revise the patterns of order required for it to conduct scientific investigations and to survive, as evidenced by continued systemic survival and investigatory behavior despite any environmental challenges it may encounter. This working definition provides ready answers to the capabilities and criteria issues raised earlier. These responses may be restated from the above definition as follows: (1) An advanced machine intelligence system for autonomous space exploration must possess the capability to utilize already formulated patterns of order and to devise new or revise existing patterns of order; and (2) the criteria by which to determine whether a system actually possesses intelligence is its observed ability to self-correct unsuccessful actions and eventually to act successfully in situations novel to the system. 3.3.2 A Systems Approach to Intelligence

Systems analysis may be used to translate the above definition into practice. Stated in general terms, the design goal is to achieve a machine intelligence capability to autonomously conduct scientific investigations and ensure mission survival. "Intelligence" can be an omnibus term which refers to a broad range of abilities including "knowing," "emoting," "fantasizing," etc. However, only rational cognition such as "knowing" is immediately relevant to machine intelligence for space exploration.

Of course, "knowing" is itself an omnibus term having a range of usages differing somewhat in meaning. In the present context it refers to the rational dimensions of intelligence, the processes of acquiring justified, though possibly fallible, statements about the world and its constituents. Among those dimensions are (1) identifying things and processes, (2) problem-solving, and (3) planning, since the outcomes of each of these processes are statements about the world selected from among a number of alternatives and justified on some basis. The essence of "knowing" in the context of a given environment is the ability to organize and thereby reduce the complexity and variety of perceived events, entities, and processes in the surroundings - a broad general class of rational activity required for machine-intelligent space exploration systems.

A "classification scheme" is any distinction or set of distinctions which can be used to divide events, entities, or processes into separate classes. By this measure taxonomies, analytical identification procedures, scientific laws and theories (e.g., "F = ma" names, hence, distinguishes forces and masses), decision criteria (e.g., go/no-go configurations in a given context), and concepts (e.g., "true" divides all statements into two separate classes) all are examples of classification schemes. Thus, a scheme is any statement, theory, model, formula, taxonomy, concept, categorization, classification, or other representational or linguistic structure which identifies the recurring characteristics of particular environments.

Tasks by which knowing is accomplished may be divided into two distinct types: (1) Utilization of preformulated fixed classification schemes, and (2) generation of new classification schemes or revision of old ones by formulating new components for the schemes. These two task types differ fundamentally both in the characteristics of the tasks and in the types of inference which underlie them.

When preformulated, fixed classification schemes are used, outcomes include identifications, classifications, and descriptions of events, entities, and processes occurring in the environment. These outcomes take the form of statements of the following general types: ? "X is an entity of type A." ? "Y is an instance of process B." ? "Z is a class-C event." In each case, perceived constituents of the environment are matched with the general classes of constituents into which the classification schemes divide the world. The pattern of inference underlying this type of task is the analytic comparison of actual environmental constituents with "known" assertions about general environmental characteristics. Thus, an important aspect of the utilization of classification schemes is the confrontation of these schemes with the facts of experience. Knowing of this type cannot be successful ? indeed, cannot even continue if the actual state of affairs in the environment and that postulated by the classification schemes differ significantly. So, while the utilization of preformulated classes is an important type of knowing activity, the actual knowing of a given environment is deficient if the schemes are incomplete or incorrect. Knowing can be complete only when new classification schemes can be formulated and incorrect ones revised.

The creation and revision of classification schemes is the second major type of task involved in knowing. The outcomes of this task are either new classification schemes or new parts for preformulated ones. This task can, in turn, be divided into subtasks the invention of new or revised classification schemes and the testing of these schemes for completeness and correctness prior to general use. Quite different types of inference underlie these two subtasks. Testing new or revised classification schemes requires analytic comparison of the claims made by these schemes with the facts of the world, exactly the same kind of process