Case study of a knowledge-based system which plans molecular genetic experiments

A logic-based approach to planning recombinant DNA experiments is presented. Plans are constructed by attempting to match lists of constraints with existing molecules, with failure resulting in rule-directed decomposition of these constraints to create subproblems. Several AI techniques which are not traditionally associated with planning but which find application for this problem domain are used. These include an object-oriented paradigm to help reduce the size of the search space and to afford an approach to efficient machine learning and the use of simulation to solve aspects of the frame problem and other real-world difficulties idiosyncratic to molecular genetics. The resulting system is capable of solving tasks which could not be addressed by previous attempts at planning in this problem domain.<
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