I don't think that the probabilistic inference is (necessarily) linear if
the interval probabilities may overlap or interact with other intervals in
a non-sequential way. I think that even non-sequential methods are not
linear if the method of search-backtracking is needed.

Jim Bromer


On Wed, Apr 23, 2014 at 12:05 AM, YKY (Yan King Yin, 甄景贤)
<[email protected]>wrote:

> On Sat, Apr 12, 2014 at 4:49 PM, Anastasios Tsiolakidis 
> <[email protected]>wrote:
>
>> Disclaimer: the answer is probably several hours or days of googling
>> away, just trying to save time here.
>>
>> I haven't looked into (non probabilistic) inference engines for a long
>> time, and it looks like the landscape has changed. I consider them
>> indispensable for game-like domains (including physics), and I would like
>> to hear about the performance and subtleties of the new breed. I am working
>> towards general game playing set-ups, which I've mentioned before as a kind
>> of AGI drosophila.
>>
>
>
> ​May not answer your question, but boolean inference can be relaxed into
> probabilistic inference with interval probabilities, and that becomes a
> linear programming problem (with linear constraints on probabilities).  The
> latter can be solved in polynomial time.
>
> Probabilistic inference is actually more desirable than boolean inference,
> as far as AGI is concerned.  So the fixation on the NP hardness of boolean
> inference may be unnecessary.  On the other hand, probabilistic inference
> may offer a route to tackle the P=?NP question.
>
> As for boolean inference, I am not aware that the landscape has changed
> drastically (as far as new techniques are concerned, but some new software
> may have emerged).  The key issue here is to choose the kind of logic, for
> example whether you want propositional logic, description logic (as used by
> Semantic Web technologies), first order logic, or higher order logic.  The
> state-of-the-art engines for each kind of logic are constantly improving,
> but seems to be slowly =)
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