3.2 Some Patternist Principles 37

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 on the identification of symbolic patterns in experience or the use of them to achieve practical goals. We will look fairly carefully at these differences in Chapter 4. Pursuing the patternist philosophy in detail leads to a variety of particular hypotheses and conclusions about the nature of mind. Following from the view of intelligence in terms of achieving complex goals in complex environments, comes a view in which the dynamics of a cognitive system are understood to be governed by two main forces: e selforganization, via which system dynamics cause existing system patterns to give rise to new ones e goal-oriented behavior, which will be defined more rigorously in Chapter 7, but basically amounts to a system interacting with its environment in a way that appears like an attempt to maximize some reasonably simple function Self-organized and goal-oriented behavior must be understood as cooperative aspects. If an agent is asked to build a surprising structure out of blocks and does so, this is goal-oriented. But the agent’s ability to carry out this goal-oriented task will be greater if it has previously played around with blocks a lot in an unstructured, spontaneous way. And the “nudge toward creativity” given to it by asking it to build a surprising blocks structure may cause it to explore some novel patterns, which then feed into its future unstructured blocks play. Based on these concepts, as argued in detail in [Goe06al, several primary dynamical principles may be posited, including: HOUSE_OVERSIGHT_012953