156 8 Cognitive Synergy here we will eschew most details and focus mainly on pointing out how PLN seeks to achieve efficient inference control via integration with other cognitive processes. As a logic, PLN is broadly integrative: it combines certain term logic rules with more standard predicate logic rules, and utilizes both fuzzy truth values and a variant of imprecise probabilities called indefinite probabilities. PLN mathematics tells how these uncertain truth values propagate through its logic rules, so that uncertain premises give rise to conclusions with reasonably accurately estimated uncertainty values. This careful management of uncertainty is critical for the application of logical inference in the robotics context, where most knowledge is abstracted from experience and is hence highly uncertain. PLN can be used in either forward or backward chaining mode; and in the language intro- duced above, it can be used for either analysis or synthesis. As an example, we will consider backward chaining analysis, exemplified by the problem of a robot preschoolstudent trying to determine whether a new playmate “Bob” is likely to be a regular visitor to its preschool or not (evaluating the truth value of the implication Bob > regular_visitor). The basic backward chaining process for PLN analysis looks like: 1. Given an implication L = A — B whose truth value must be estimated (for instance L = C&P = G as discussed above), create a list (A1,...,An) of (inference rule, stored knowledge) pairs that might be used to produce L 2. Using analogical reasoning to prior inferences, assign each A; a probability of success e If some of the A; are estimated to have reasonable probability of success at generating reasonably confident estimates of D’s truth value, then invoke Step 1 with A; in place of LE (at this point the inference process becomes recursive) e If none of the A; looks sufficiently likely to succeed, then inference has “gotten stuck” and another cognitive proc