8.4 Some Critical Synergies 149 For instance, deduction might defer to the “attentional knowledge” subsystem, and make a judgment as to which of the many possible next deductive steps are most associated with the goal of inference and the inference steps taken so far, according to the HebbianLinks con- structed by the attention allocation subsystem, based on observed associations. Or, if this fails, deduction might ask MOSES (running in supervised categorization mode) to learn predicates characterizing some of the terms involving the possible next inference steps. Once MOSES pro- vides these new predicates, deduction can then attempt to incorporate these into its inference process, hopefully (though not necessarily) arriving at a higher-confidence next step. 8.4 Some Critical Synergies Referring back to Figure ??, and summarizing many of the ideas in the previous section, Table ?? enumerates a number of specific ways in which the cognitive processes mentioned in the Figure may synergize with one another, potentially achieving dramatically greater efficiency than would be possible on their own. Of course, realizing these synergies on the practical algorithmic level requires significant inventiveness and may be approached in many different ways. The specifics of how CogPrime manifests these synergies are discussed in many following chapters. heracrimotor Uncertain inference Creaios new comcepis Goal refiners Simadations pervs | Cronies new concepie ari Peiakonehipa enables ware careha a mathad of lesting and elatonshes anabing briafer usah eal -based nlavsoce poe Du ial snabkng beale: ueghs rierence trade prurung wri bee terial Pfarnce wade ~Siwialions Saigeat hyypaal eens to bo explored wis aril tere Supervised Cresios now Goal refinermnerd allies Siealation prowdes Extraction of serworimaior procedure learning proceduren in be umed more proce detniieon amethed of “fitness | pafiema aficws craton of 4 motes © cf Girenoe fierectene bmaton” alcwing abstracted Hines