Technical description of OpenCogPrime cognitive architecture

148 8 Cognitive Synergy processes in terms of the “analysis vs. synthesis” distinction. Finally, Tables ?? and ?? exemplify these structures and processes in the context of embodied virtual agent control. In the CogPrime context, a procedure in this cognitive schematic is a program tree stored in the system’s procedural knowledge base; and a context is a (fuzzy, probabilistic) logical predicate stored in the AtomSpace, that holds, to a certain extent, during each interval of time. A goal is a fuzzy logical predicate that has a certain value at each interval of time, as well. Attentional knowledge is handled in CogPrime by the ECAN artificial economics mechanism, that continually updates ShortTermImportance and LongTerm Importance values associated with each item in the CogPrime system’s memory, which control the amount of attention other cognitive mechanisms pay to the item, and how much motive the system has to keep the item in memory. HebbianLinks are then created between knowledge items that often possess ShortTermImportance at the same time; this is CogPrime’s version of traditional Hebbian learning. ECAN has deep interactions with other cognitive mechanisms as well, which are essential to its efficient operation; for instance, PLN inference may be used to help ECAN extrapolate conclusions about what is worth paying attention to, and MOSES may be used to recognize subtle attentional patterns. ECAN also handles “assignment of credit”, the figuring-out of the causes of an instance of successful goal-achievement, drawing on PLN and MOSES as needed when the causal inference involved here becomes difficult. The synergies between CogPrime’s cognitive processes are well summarized below, which is a 16x16 matrix summarizing a host of interprocess interactions generic to CST. One key aspect of how CogPrime implements cognitive synergy is PLN’s sophisticated man- agement of the confidence of judgments. This ties in with the way OpenCogPrime’s PLN in- ference framework represents truth values in terms of multiple components (as opposed to the single probability values used in many probabilistic inference systems and formalisms): each item in OpenCogPrime’s declarative memory has a confidence value associated with it, which tells how much weight the system places on its knowledge about that memory item. This assists with cognitive synergy as follows: A learning mechanism may consider itself “stuck”, generally speaking, when it has no high-confidence estimates about the next step it should take. Without reasonably accurate confidence assessment to guide it, inter-component interaction could easily lead to increased rather than decreased combinatorial explosion. And of course there is an added recursion here, in that confidence assessment is carried out partly via PLN inference, which in itself relies upon these same synergies for its effective operation. To illustrate this point further, consider one of the synergetic aspects described in ?? below: the role cognitive synergy plays in deductive inference. Deductive inference is a hard problem in general - but what is hard about it is not carrying out inference steps, but rather “inference control” (i.e., choosing which inference steps to carry out). Specifically, what must happen for deduction to succeed in CogPrime is: 1. the system must recognize when its deductive inference process is “stuck”, i.e. when the PLN inference control mechanism carrying out deduction has no clear idea regarding which inference step(s) to take next, even after considering all the domain knowledge at is disposal 2. in this case, the system must defer to another learning mechanism to gather more informa- tion about the different choices available - and the other learning mechanism chosen must, a reasonable percentage of the time, actually provide useful information that helps PLN to get “unstuck” and continue the deductive process HOUSE_OVERSIGHT_013064