Page:Advanced Automation for Space Missions.djvu/356

 However,thesametheoretical technologyutilizesa theoreticalmodelof therealworldprocessconnecting..... with and (11Ij) A derives rules of production for A from the model. Examples of theoretical technology include radar, lasers, the Polaroid Land camera, digital computers, and integrated circuits. Although these two patterns are distinct, many specific technologies have a "mixed mode" pattern of development. In such cases, a model of the full process connecting (It ..... Ij) with A is still not available, but refinements and extensions of the empirically discovered rules of production are based on partial models of the process. That is, some decomposition of the full process into its subprocesses has been made, and models for these subprocesses have been constructed. This is not true theoretical technology, however, because no general model of the full process is available and, consequently, an integrated set of model-derived rules of production is not possible. Empirical technology, but not theoretical technology, is ultimately self-limiting within any given field of technology. That is, there is a level of technological capability beyond which empirical techniques cannot penetrate. This level is a function of empirical technology's pattern of development, not of the world itself. The reason for this self-limiting characteristic is the absence of theoretical models. Empirical methods develop via trial and error, small incremental refinements and extensions of empirically discovered rules of production. Since the rules are not based on a model of real-world processes, however, these modifications cannot be orchestrated and integrated, but are instead ad hoc "fixes" that hold only over a limited domain. Once the modified empirically based rules of production reach a sufficient level of complexity, the probability becomes very high that the next ad hoc "fix" may undo a previous one. Further development in the particular technological field (development in the sense of increased technical capability) stops at this point. Theoretical technology need not be self-limiting. Since it is based on a model, the above effect may not be present. Theoretical technology is thus able to push technological development in a given field to the maximum extent consistent with whatever real-world limitations characterize the field. This discussion sets the stage for a consideration of the type of intelligence capability which can realistically be expected from machine intelligence research. The question of machine intelligence has been replaced by the question of the machine formulation of hypotheses. If we define a scale of hypothesis formulating capability (HYP) as HYP -TH + CRED, where TH is the theoretical content of the hypothesis and CRED is the credibility of the hypotheses, then the design goal for advanced forms of machine intelligence is to be as high on this scale as possible. Either an empirical technology or a theoretical technology pattern can be followed in developing machine intelligence. However, with respect to the HYP-level which can be achieved by the two different patterns, the empirical technology approach is ultimately self-limiting at a level of hypothesis formulating capability which is lower than that prerequisite for automated space exploration (see sections 3.2 and 3.3). It is clear that automated space exploration and other applications requiring very advanced machine learning are possible only if the theoretical technology approach to machine intelligence is employed. Unfortunately, AI is currently taking the empirical technology approach to hypothesis formulation. There is nothing mysterious about the theoretical approach it may be started by research into the 'patterns of logical inference by which hypotheses are formulated. Such an approach is limited only by the degree to which hypothesis formulation is logical and inferential. On the condition that it is, then the theoretical technology does not face a real world barrier to achieving full machine-hypothesis generating capability.

6. 2.4 Initial Directions for NASA Several research tasks can be undertaken immediately by NASA which have the potential of contributing to the development of a fully automated hypothesis formulating ability needed for future space missions: (1) Continue to develop the perspective and theoretical basis for machine intelligence which holds that (a) machine intelligence and especially machine learning rest on a capability for autonomous hypothesis formation, (b) three distinct patterns of inference underlie hypothesis formulation -analytic, inductive, and abductive inference, and (c) solving the problem of mechanizing abductive inference is the key to implementing successful machine learning systems. (This work should focus on abductive inference and begin laying the foundations for a theory of abductive inference in machine intelligence applications.) (2) Draw upon the emerging theory of abductive inference to establish a tem_inology for referring to abductive inference and its role in machine intelligence and learning. (3) Use this terminology to translate the emerging theory of abductive inference into the terminology of stateof-the-art AI; use these translations to connect abductive inference research needs with current AI work that touches on abduction, e.g., nonmonotonic logic: and then discuss these con-_ctions within the AI community. (The point of such an exercise is to identify those aspects of current AI work which can contribute to the achievement of mechanized and autonomous abductive inference systems, and to identify a sequence of research steps that the AI community can take towards this goal.) (4) Researchfor specificintelligence proposalsmachineprojectsshouldproject explainhowtheproposedcontributesto theultimategoalof autonomousintelli machinegencesystemsofanalytic,whichlearnbymeansinductive, andabductiveEnoughaboutthe