Comparing the effect of pruning on a best path and a naïve-approach blackboard solver

A naïve solver is one approach that can be used to identify prospective solutions based on data on (or projected to be on) a Blackboard Architecture’s blackboard. The naïve solver approach doesn’t implement heuristics or other techniques to determine what solution paths to attempt first. Instead, it runs the blackboard forward (simulating what would occur if data were gradually added to the blackboard at a faster-than-real time rate). The approach doesn’t guarantee that an optimal solution will be found and will need to be run repetitively to create multiple solutions for comparison. This paper assesses the effect of pre-pruning the blackboard’s facts and rules to remove those that are not relevant (e.g., facts that cannot be asserted, rules that cannot be triggered) or which produce irrelevant facts and pruning actions that produce irrelevant facts (and/or trigger other similarly useless actions). It describes the Blackboard implementation and its utility, explains the pruning process used and presents quantitative and qualitative assessment of the utility of pruning to a naïve solver’s operations. This value is extrapolated to facilitate consideration of a more robust pruning process which also removes low-value facts, actions and rules in addition to those being removed due to their uselessness.