60 4 Brief Survey of Cognitive Architectures 4.2.1 SOAR The cognitive architectures best known among AI academics are probably Soar and ACT-R, both of which are explicitly being developed with the dual goals of creating human-level AGI and modeling all aspects of human psychology. Neither the Soar nor ACT-R communities feel themselves particularly near these long-term goals, yet they do take them seriously. Soar is based on IF-THEN rules, otherwise known as “production rules.” On the surface this makes it similar to old-style expert systems, but Soar is much more than an expert system; it’s at minimum a sophisticated problem-solving engine. Soar explicitly conceives problem solving as a search through solution space for a “goal state” representing a (precise or approximate) problem solution. It uses a methodology of incremental search, where each step is supposed to move the system a little closer to its problem-solving goal, and each step involves a potentially complex “decision cycle.” In the simplest case, the decision cycle has two phases: e Gathering appropriate information from the system’s long-term memory (LTM) into its working memory (WM) e A decision procedure that uses the gathered information to decide an action If the knowledge available in LTM isn’t enough to solve the problem, then the decision procedure invokes search heuristics like hill-climbing, which try to create new knowledge (new production rules) that will help move the system closer to a solution. If a solution is found by chaining together multiple production rules, then a chunking mechanism is used to combine these rules together into a single rule for future use. One could view the chunking mechanism as a way of converting explicit knowledge into implicit knowledge, similar to “map formation” in CogPrime (see Chapter 42 of Part 2), but in the current Soar design and implementation it is a fairly crude mechanism. In recent years Soar has acquired a number of additional methods and mo