To pick up roughly where I left off...
Tactical theorem provers such as HOL Light and Coq are the modern-day epitome of procedural logic, in some ways. They work by the simple lack-of-mechanism which I gave as the minimal procedural logic in the previous post: they are simply programmable theorem provers. "Tactics" (aka "strategies") are created, which are simply programs for guiding inference; tactics can use each other, so that in a weak sense they are like goals and subgoals. What's really nice about them is that they cannot be made to return an incorrect result, no matter how wild the tactic. What they lack compared to SOAR and PLANNER is:
- Reflexive inferences: SOAR automatically forward-chains for all rules in its KB. In a similar way, Prolog automatically backwards-chains for all programmed rules. PLANNER was designed to do both, with a declaration of which way a rule should be used upon its creation. (SOAR does something similar with a little work.) ACL2 probably has the most advanced facilities, from my cursory examination; there are many categories of possible reflexive inferences for different sorts of theorems, and the system has intelligent defaults which the user can override. However, the reflexive inferences are still clearly too simplistic sometimes; for example, the user can create an infinite loop just by proving both B = A and A = B... ACL2 will happily swap A for B indefinitely the next time either one appears in a statement.
- Truth Maintenance: Some facts are of the sort that can change. Under strict logic, we should index them by time to account for this, but it seems necessary for efficiency to ignore this and just change the facts directly. If that's the strategy, then we have a truth maintenance problem: we may need to update our entire KB to reflect the change, withdrawing any statements we proved based on the now-false information. In SOAR, this is reflected in the distinction between o-support and i-support: o-support represents that the rule is intended as making a change to the KB in firing, which cannot be undone except by another o-rule, whereas i-support represents that the rule's consequences should be undone immediately when the premises become false (thanks to an o-rule changing something). This is probably the most advanced form of this sort of distinction. PLANNER also addressed the issue, however. (That's why I called SOAR a "descendant of planner" in my previous post on this topic; since then, though, I've asked Paul Rosenbloom about it. He indicated that PLANNER did not actually influence SOAR in any significant way, so it looks like SOAR came up with the idea quite independently.)
Paul Rosenbloom's probabilistic-relational rethink of SOAR (see his publication page) takes the position that #2 can emerge in a simple way from the sum-product belief propagation algorithm; basically, this amounts to re-estimating all the weights in working memory whenever a change is made by a rule in long-term memory. The system also deals with time in additional ways. As for #1, there is some divergence from SOAR's system, but nothing as sophisticated as ACL2. Paul Rosenbloom's proposal does not have a reason to focus on proofs via mathematical induction in the way ACL2 does, so it would not benefit from a translated version of ACL2's guidance system... perhaps it's reasonable to say that much of the sophisticated stuff in ACL2 is fairly special-purpose for its domain of application.
Having sum-product as a basic dynamic seems like a good idea, but at the same time, I'm thinking of a system which can reason about inference guidance even for that. For example, it seems reasonable to (A) weigh the product-sum algorithm's attention to specific areas of the belief network based on multiple factors, including which variables are currently undergoing the most change, and which variables are most critical for the goals; (B) augment the product-sum algorithm with more accurate algorithms in areas of the belief network where accurate estimation is critical. In the Rosenbloom system, (A) would have to be an addition to the basic level, but he has discussed (B) as something which might be implemented on top of the basic level using rules. The second situation is preferable, but at the same time, any "basic level" of reactive inference/decision will have some built-in assumptions. There is a way around this, however: dynamic replacement of inference strategies in the manner of Hutter's Algorithm. Hutter's algorithm is tremendously inefficient, so it would be nice to find a small-scale, incremental way of employing the same trick. (Also, Hutter's algorithm would need to add probabilistic reasoning about algorithm speed and correctness, to satisfy the desire for a fully probabilistic-relational system.)
In any case! The main point is, in probabilistic relational systems, truth maintenance gets replaced by some form of belief revision plus some way of dealing with time. (In OpenCog, I understand that part of the strategy will be to have the truth values of time-sensitive statements decay gradually as they become more probably out-of-date.)
The other issue to address is how a goal system for a probabilistic-relational version should work, but, I guess that's too big a topic for the moment... though honestly I started writing this post to talk about that :) Such is blogging.