| Much evidence supports the use of schemata as a basic element of human problem solving. Yet, little work has been done to show how schematic information can be represented, manipulated or used for problem solving.;A schema maps goals to abstract action sequences. Schema-based problem solving is the process of identifying appropriate schemata, and then adapting that schematic knowledge to the particular circumstances of a problem. Schemata adapt to the problem environment in three ways: (1) Multiple schema expansions are pursued simultaneously and only the best expansion is selected for execution; (2) As a schema expands, constraints defining and limiting future expansion are established. Constraint satisfaction ensures that the plan will be internally consistent and still attain the goal; (3) Schema components may be selectively deleted or integrated in response to goal requirements or constraints set up during problem solving. The three methods translate a selected schema into a fully-developed plan.;However, problem solving in complex domains requires an ability to switch rapidly and reliably between problem solving and error recovery. Plan generation and execution are tightly interwoven to allow dynamic error recovery through plan alteration and replanning. A fully-developed problem solution never exists at any one time. Instead, the plan constantly evolves in response to execution and problem solving requirements. Because deviation from a plan due to inaccurate world models or incorrect planning is so common, error recovery at many levels is seen to be a normal, rather than exceptional, part of problem solving. Expanding alternate plans in parallel and distributing control over many sites allows the system to be responsive to error and quick in execution.;Methods for deriving plans from schemata and a problem solving philosophy are demonstrated in a program. SHEM, which generates and executes plans to assemble small block figures in a simulated world. |
