88 4 Brief Survey of Cognitive Architectures
e foundational use of uncertainty in reasoning
One can create an analogy between LIDA’s workspace structures and codelets and a logic- based architecture’s assertions and functions. However, LIDA’s codelets only operate on the structures that are active in the workspace during any given cycle. This includes recent percep- tions, their closest matches in other types of memory, and structures recently created by other codelets. The results with the highest estimate of success, i.e. activation, will then be selected.
Uncertainty plays a role in LIDA’s reasoning in several ways, most notably through the base activation of its behavior codelets, which depend on the model’s estimated probability of the codelet’s success if triggered. LIDA observes the results of its behaviors and updates the base activation of the responsible codelets dynamically.
We note that for this kind of uncertain inference/activation interplay to scale well, some level of cognitive synergy must be present; and based on our understanding of LIDA it is not clear to us whether the particular inference and association algorithms used in LIDA possess the requisite synergy.
4.5.9.2 LIDA versus CogPrime
The LIDA cognitive cycle, broadly construed, exists in CogPrime as in other cognitive archi- tectures. To see how, it suffices to map the key LIDA structures into corresponding CogPrime structures, as is done in Table 4.1. Of course this table does not cover all CogPrime processes, as LIDA does not constitute a thorough explanation of CogPrime structure and dynamics. And in most cases the corresponding CogPrime and LIDA processes don’t work in exactly the same way; for instance, as noted above, LIDA’s action selection relies solely on LIDA’s “activation” values, whereas CogPrime’s action selection process is more complex, relying on aspects of CogPrime that lack LIDA analogues.
4.5.10 Psi and MicroPsi
We have saved for last the architecture that has the most in common with CogPrime : Joscha Bach’s MicroPsi architecture, closely based on Dietrich Dorner’s Psi theory. CogPrime has borrowed substantially from Psi in its handling of emotion and motivation; but Psi also has other aspects that differ considerably from CogPrime. Here we will focus more heavily on the points of overlap, but will mention the key points of difference as well.
The overall Psi cognitive architecture, which is centered on the Psi model of the motivational system, is roughly depicted in Figure 4.14.
Psi’s motivational system begins with Demands, which are the basic factors that motivate the agent. For an animal these would include things like food, water, sex, novelty, socialization, protection of one’s children, and so forth. For an intelligent robot they might include things like electrical power, novelty, certainty, socialization, well-being of others and mental growth.
Psi also specifies two fairly abstract demands and posits them as psychologically fundamental (see Figure 4.15):
® competence, the effectiveness of the agent at fulfilling its Urges e® certainty, the confidence of the agent’s knowledge
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