Universal Weak Method
Soar was used to posit the existence of a universal weak method, an
approach that said that the search strategy should arise out of the
interaction between the structure of the agent and the proposed task.
The search strategy chosen is assumed to be weak; i.e., the
agent has little knowledge about the task environment. Thus, any of the
weak methods may arise in Soar out of the interaction of this universal
weak method and the task. The advantage of such an approach is that
it avoids program synthesis: the behavior results from the interaction
of knowledge and task rather than being explicitly programmed.
The following is a summary of some of the weak methods that have been
demonstrated in Soar using the idea of a universal weak method
(adapted from
Laird, Newell and Rosenbloom, 1987):
- Heuristic Search
- Operator Subgoaling
- Waltz Constraint Propagation
- Means-End Analysis
- Generate and Test
- Breadth-First Search
- Depth-First Search
- Lookahead Search
- Simple and Steepest Ascent Hill-Climbing
- Progressive Deepening
- Mini-Max
- Alpha-Beta Pruning
- Iterative Deepening
- Branch and Bound
- Best-First Search
- Macro-operators
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