Approach: Program Supervision

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Many different sets of programs have been developed in disciplines like signal processing, image processing, or scientific computing. Application of such packages implies technical and practical knowledge, acquired by experts as personal expertise. To achieve a processing objective, several programs have to be judiciously organised in a plan and their execution has to be monitored in order to perform the best possible treatment. Inexperienced users often find these programs difficult to handle, as they do not master processing techniques enough to make appropriate decisions.
Nevertheless, expertise can be identified, collected from experts or extracted from programs, and capitalised in a structured way so as to be transfered to less experienced users.

Program supervision (PS) aims at automating the management of the decisions involved in the skilled use of a program set, such as appropriate program choice and organisation, parameter setting, execution control or the handling of methodological details. Within the framework of a knowledge-based system (KBS), such a system captures the expertise and emulates the reasoning of an expert when handling the programs, thus freeing the user from dealing with pure processing and computational details. More than a user interface, PS offers means to reuse both static knowledge (i.e. ontology) and strategy (i.e. problem-solving method) involved when solving a problem with them.

A KBS for PS (or PS system) consists of:

a set of executable programs,
a knowledge base that holds expertise about the use of programs for different processing objectives,
an engine that automates the planning and the control of execution of the programs to solve processing objectives,
a user interface for the query of problem specification, initial data and other necessary information, and the supply of (visual) user-level results.

PS task is solved by a problem-solving method adapted to experts' reasoning and domain requirements. This method is implemented by the PS engine, that roughly automates a cycle of four reasoning phases to solve user's problem:

planning of (part of) the solution by choosing and organising programs into a sequence of actions,
controled execution of each action after appropriate refinement and parameter tuning,
evaluation of the produced results,
repair of the (partial) solution if results are not satisfying with appropriate correcting actions.

Whatever strategy the program supervision engine implements, it relies on the contents of the knowledge base, structured by concepts belonging  to the program supervision ontology, conveniently chosen to represent the relevant part of the domain ontology. A PS knowledge base holds sufficient knowledge on the programs to select them, schedule them, compute the values of their parameters and run them to solve different objectives. Hence, PS ontology comprises concepts that are shared among the planning and the software engineering and reuse communities.

Goals describe an abstract functionality to achieve, together with constraints on the expected final state;
Operators either represent executable programs (i.e. primitive operators) or known combinations of operators that solve abstract processing steps (i.e. complex operators). Combinations are described at different abstraction levels: they can be for instance a sequence of operators, a choice between alternative operators, etc. Both kinds of operator descriptions mention input and output data arguments (including parameters), abstract functionality that they accomplish, conditions under which they are applicable (i.e. preconditions) and effects on current state of the system. A primitive operator also includes the calling interface of its corresponding program to execute.
Attached to operator description, various expert criteria (e.g. rules, fonctions) enclose decision knowledge for PS solving. They enable to state the way to initialise or adjust operator parameters, to evaluate the results of program execution or to repair unsatisfactory processing. Attached to complex operators, they also capture the knowledge about the choice between alternative operators that solve the same functionality, for example.
These criteria often reason about a description of data (i.e. their type and contents) processed by programs, and the current state of the system.

   More about PS:

ORION references.
Related work

MVP: NASA Jet Propulsion Laboratory, USA.
Collage: NASA Ames, USA.
VISIPLAN: Laboratory for Computer Science Research, Rutgers University, USA.
SCARP and PowerTask: SHERPA Team, INRIA Grenoble, France.
BORG: GREYC, Caen, France.
VSDE: VIDIMUS european project.
IAMI: Brest University, France.
IRIT, Toulouse University, France.
Chandra Shekhar's work at CVL, CFAR, University of Maryland, USA.
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