Two best planners from this planning competition are not based on ASP
https://ipc2018-cla <https://ipc2018-classical.bitbucket.io/scores.html>s
<https://ipc2018-classical.bitbucket.io/scores.html>
sical.bitbucket.io/scores.html
<https://ipc2018-classical.bitbucket.io/scores.html>, first one is based on
deep-learning and second one on a heuristic with a pattern database. In
2014 half of the planners were based on IBM CPLEX solver.. i guess the
reason is that one can't have a universally optimal solver for np-hard
problems, but one can find some shortcuts for a reasonably small subset of
them.

ср, 28 апр. 2021 г. в 22:25, Linas Vepstas <[email protected]>:

> Ben,
>
> On Wed, Apr 28, 2021 at 1:59 PM Ben Goertzel <[email protected]> wrote:
>
>>
>>>
>>> Now, what I say above is "easy to say" but is "hard to do" --
>>> implementing what I suggest is a large project.  But then, in software,
>>> nothing is free. facebook and google and amazon employ thousands of
>>> engineers because writing good software is hard. Imagining that you can
>>> create a new planner out of thin air in a few months is not a realistic
>>> dream. Don't repeat history; learn from it.
>>>
>>>
>> As I understand what is being proposed here is a student research project
>> not a large-scale engineering project...
>>
>
> Anatoly implied that he wants to create a new planner in hyperion.
> Creating hyperion, and a new planner for it, using "more natural"
> expressiveness, is .. well, sure you can treat hyperon as a student project
> consisting of throw-away code. But the way you talk about hyperon is as if
> it is going to be a replacement for the atomspace, and not as some
> experimental throw away code.
>
> Anyway, I'm not really talking about throw-away code; I am just saying
> that comp sci journals, such as the "Journal of Optimization" have
> thousands of articles on planning algorithms, developed over many decades;
> Ignoring these, and walking in with a clean-slate hyperon design ... well,
> don't be naive.
>
>
>>
>> Exploring constraint-satisfaction-based planning makes sense, but for
>> some planning domains this approach may not be best.  E.g. if you're
>> planning in a highly dynamic environment (as faced say by robots moving
>> around in a house or on the street) then I'm not sure the available
>> constraint satisfaction algos can deal well w/ the needed real-time plan
>> updating...?
>>
>
> (1) ASP will solve most planning and constraint satisfaction problems in
> milliseconds.   So you can do a 60-frames-per-second update rate if you
> wish.
>
> (2) It's not like people haven't done this before. I went to some talk, I
> think in HKSTP, where someone talked about using SLAM (simultaneous
> localization and mapping) from ultrasound sensors on some robot, and then
> hooking that into ASP (specifically, the Potsdam solver was mentioned) to
> perform basic reasoning tasks ("if the door is closed, try to push the
> door", "if the door won't push try to pull the door" "else reach for the
> doorknob") -- and this was not the first time I've heard of ASP being
> integrated in with probabilistic systems in robotics -- talk titles tend to
> be along the lines of "how to integrate probabilistic and crisp logic
> reasoning in robotics" or something similar.
>
>
>> My 2 tokens worth...
>>
>
> Are these one of those bitcoin tokens that everyone is talking about these
> days?
>
> --linas
>
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