3.2 Some Patternist Principles 37 e What learning processes are utilized for recognizing patterns? e What mechanisms are used to give the system the ability to introspect (so that it can recognize patterns in itself)? Now, these same sorts of questions could be asked if one substituted the word “pattern” with other words like “knowledge” or “information”. However, we have found that asking these ques- tions in the context of pattern leads to more productive answers, avoiding unproductive byways and also tying in very nicely with the details of various existing formalisms and algorithms for knowledge representation and learning. Among the many kinds of patterns in intelligent systems, semiotic patterns are particularly interesting ones. Peirce decomposed these into three categories: ® iconic patterns, which are patterns of contextually important internal similarity between two entities (e.g. an iconic pattern binds a picture of a person to that person) e indexical patterns, which are patterns of spatiotemporal co-occurrence (e.g. an indexical pattern binds a wedding dress and a wedding) e symbolic patterns, which are patterns indicating that two entities are often involved in the same relationships (e.g. a symbolic pattern between the number “5” (the symbol) and various sets of 5 objects (the entities that the symbol is taken to represent)) Of course, some patterns may span more than one of these semiotic categories; and there are also some patterns that don’t fall neatly into any of these categories. But the semiotic patterns are particularly important ones; and symbolic patterns have played an especially large role in the history of AI, because of the radically different approaches different researchers have taken to handling them in their AI systems. Mathematical logic and related formalisms provide sophisticated mechanisms for combining and relating symbolic patterns (“symbols”), and some AI approaches have focused heavily on these, sometimes more so than