Human Factors in Automated and Robotic Space Systems Proceedings of a Symposium (1987) / Chapter Skim
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AI Systems in the Space Station
AI Systems in the Space Station
Pages 91-112
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From page 91... ...
At the same time, the need for intelligent communication and shared responsibility between such computer programs and space station residents poses a serious challenge to present interfaces between man and machine. Hence, the Potential and nope for contributions from AI to the space station effort is great.
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From page 92... ...
Ccmputer-based methods for automatically updating such procedures, given updates to the description of the space station, would greatly enhance the ability to manage the evolving station. The crew of the space station will possess differing levels of expertise regarding different space station subsystems, and will live in the station long enough that then' expertise will change over the course of their stay aboard the Station.
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From page 93... ...
For example' one might well expect that a computer could monitor various space station subsystems such as the parts of the navigation system, to detect behavior cutside their expectcS operating ranges, take remedial actions to contain the effects of observed errors, diagnose the likely causes of the observed symptoms, and reconfigure the system to eliminate the error. Of course, lim;t=~ applications of computers to this king of problem are fairly common in current-day space systems.
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From page 94... ...
The basic idea behind this troubleshooting system is that it uses the schematic of the system, together with its knowledge of the expected behaviors of system Opponents, ~ order to reason backward from observed incorrect output signals to those upstream circuit Opponents that could have product the cbservec} error. This process is i'1ustrat~ in Figure 1, tin from Davis (1984~.
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From page 95... ...
. The topic of reasoning about the expected behavior of designed artifacts of many types is an active research area within AI (see, for example, the recent special volume of Artificial Iht=1ligence on qualitative reasoning about physical systems (North-Holland, 1984~.)
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From page 96... ...
~ this case, the s~risor card take ye form of a collection of mobile platforms whose sensors include eras, range finders, Much sensors, and oscilloscope pubes, and whose effe~ors include gels, r~et engines, manipulators, signal generators, arm arc welders. Such a system might be ~1 to zanier the Physical plant of the space station, c)
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From page 97... ...
vantage point, and using tools to manipulate the space station. Thus, NASA Should consider supporting research on the generic problems of Harrison su~isory system;, as well as research on selected instances of the problem which it expects would yield significant practical gains.
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From page 98... ...
On the other hand, one can make due with observing only a small proportion of the signal values in a circuit and ,,.c~ the model of suboc~ponent behaviors to infer additional signal values upstream and downstream of observed signals. Given the various uncerta Sties that must be faced by a supervisory system, it is unlikely that purely algorithmic methods can be mapped out for dealing with all eventualities (although the vast NASA troubleshooting manuals indicate the degree to which this ought be possible)
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From page 99... ...
also suggest the Importance of including humans in the problem-solving process. Even by optimistic estimates, it seems unlikely that AI systems will be able to completely replace human judgement in many supervisory tacks, though they may well augment it in many tasks.
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From page 100... ...
New research is needed to develop planning methods that are robust with respect to uncertainties of the kinds discussed above. One usefrn research task here would be to develop methods that pro~u~plans which include sensor operations to reduce anticipated uncertainties in the results of effecter actions, and that include conditional branches in the plan to allow for "run-time" derisions based on sensory actions.
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From page 101... ...
An example research task here might be to develop a system that employs a number of video cameras, and which determines the correspondence between image features of the various images. A more ambitious project sight try to predict image features likely to be found by one camera, based on information from other touch, video, and heat sensors.
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From page 102... ...
If ever we reach the stage of a fully automated, self-supporting space station, we are likely to first spend a significant period of time in which computer assistants will provide certain fu~ly-automated services (e.g., simply monitoring station subsystems to watch for unexpected behavior) , but will require Interaction with their human counterparts in responding to many navel
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From page 103... ...
In the space station, we may find it desirable to share responsibility On motor tasks, as in a human controlling the mechanical robot arm in the space shuttle, in cognitive hanks, as in a human an] computer system working jointly to troubleshoot a failed power supply, or In perceptual tasks, In which a human may assist the computer in finding corresponding pa Mets in multiple camera images so that the co mput~r can then apply image analysis and enhancement procedures to the images.
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From page 104... ...
LEAP can then analyze this circuit, verify that it correctly implements the desired function, and formulate a generalized rule that will allow it to raccmm£nd this circuit in similar subsequent situations. The key to LEAP's ability to learn general rules from specific examples lies in its starting knowledge of circuit operation.
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From page 107... ...
Once it has verified that the user's circuit correctly implements the desired function, then it can generalize on this action by retaining only those features of the specific situation that are mentioned in this explanation. Similarly' if one tried to construct such a learning apprentice for troubleshooting power supply faults, one would want to include sufficient initial knowledge about the power supply (i.e., its schematic)
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From page 108... ...
m e continually changing configuration of the station itself, the continN ally changing crews and types of operations that will be conducted aboard the space station, the evolving technology that will be present, all dictate that the compute' assistants aboard must be able to adjust to new problems, new procedures an] new problem solving strategies over the life of the space station.
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From page 109... ...
inseam tom de~relc ping hernia apprentice sy~ for Apace station applications is arranged bash on reaent AT rams and on me importance of such systems to Ache Apace station pa ~ r am. A prudent rester ~ she ategy at this point woNId be to support develcpment of a variety of learning apprentices in various task areas (e.g., for troubleshooting space station subsystems, for monitoring and controlling subsystems, for managing robot manipulation of its environment)
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From page 110... ...
Such research is Important both because of its potential impact on reliability and safety of the space station and because the technical development of the field of AI is at a point where a push in this area may yield significant technical advances. Such hands-on supervisory systems could include both physically stationary supervisory systems that mom tor electronic subsystems, power supplies, navigation subsystems and the like, as well as physically mobile supervisors that monitor and repair the exterior and inferior physical plant of the space station.
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From page 111... ...
IN - -A 1984 Artificial Intelligence, Special Volume on Oualitative Reasoning About Physical Systems. Nor~h-Holland.
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From page 112... ...
Pp. 673-680 in Frcceedings of the Ninth International Joint Conference on Artificial Intelligence.
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