Let's say we have a person trying to walk from east to west, but a north-south wall is in the way. Let's also say that the wall is longer north than it is south (relative to the person). What options are there to receive the best possible heuristic values, and make sure he walks towards the south end of the wall instead of the north?
Thank you.
I guess you mean path planning with A* or similar. The simplest heuristic is just the real distance from your person without obstacles to the goal. With this heuristic you will always find the shortest path to the goal.
For a deeper look into this topic I recommend to read this.
Related
(Just a heads up, this feels like a weird question to ask since there's not really any code involved, I'm not sure if this is the right place to ask)
I am trying to summarise a journal entry and convert it into second person past tense (i.e. "I went to the shop" -> "You went to the shop").
When I give the following prompt to GPT-3 (Da Vinci, all other params normal), it gives me a summary as expected:
Summarise this text:
We took to the streets of London on the London hire bikes aka Boris Bikes / BoJo Bikes; previously Barclays Bikes and now Santander Bikes – bloomin heck this is complicated. I knew the direction where I wanted to get to and knew how to get there except I didn’t really.
We started our journey at one of bike hire station in St John’s Wood and continued around Regents Park (the wrong way) Simon got us to one of the gateways to the path along the Regents Canal. Sometimes they can be quite difficult to find; this was one of those times. This particular one was located at the back of a housing estate; only that Simon knew where it was there was no way I would have found it.
Off down the canal we went. Sunday afternoons are a busy time along the canal with local people mixed in with tourists from all over the world; so cycling along a narrow path is not easy as everyone walks on different sides of the path (according to where they come from)! We got towards Camden Market and the path got very busy, to the point that I almost went into the canal but with a wibble and a wobble I managed to stay in. At that point the decision was easily made to get off that bike and walk it. The Santander App showed us where the nearest parking station was and that there was space available to park up.
Coffee time! Forget the major chains, we found a small local place called T & G for some cups of coffee and a sarnie before we went out to find out next bike to get us to Granary Square in Kings Cross for our next stop. From the canal path there is a grassed set of steps going up to the Square but first we parked up the bikes on the other side of the canal. So many places to choose from to hang out, for drinks and for food or trains to Paris, Lille, Edinburgh or Manchester to start off with.
All in all, we went out and achieved what we intended to – a cycle along the canal with a couple of stops along the way for some food and drinks. What better way to spend a Sunday afternoon.
// GPT-3's answer:
The author takes a Boris Bike (a bike available for rent in London) and cycles along the Regents Canal. They note that the path is busy on a Sunday afternoon, but manage to find a parking spot for their bike before exploring the area around Granary Square in Kings Cross.
That is a very good summary.
Now, I can convert the summary to second person POV like so:
Convert the following to past tense second person:
The author takes a Boris Bike (a bike available for rent in London) and cycles along the Regents Canal. They note that the path is busy on a Sunday afternoon, but manage to find a parking spot for their bike before exploring the area around Granary Square in Kings Cross.
// GPT-3's answer:
You took a Boris Bike and cycled along the Regents Canal. You noted that the path was busy on a Sunday afternoon, but managed to find a parking spot for your bike before exploring the area around Granary Square in Kings Cross.
Again, excellent! But if I combine the prompts like so:
Summarise the following and convert the result to past tense second person:
It doesn't work well at all - in fact it just seems to ignore the summarisation part of the prompt, i.e. it just converts the whole passage to second person past tense. How can I fix this?
Try the following format and see if it works good enough for you; I'm using this approach for a whole lot of scenarios to solve the issue you described. Use zero-temperature (unless you want to risk variations).
Our prompt:
Story Text: "We took to the streets of London on the London hire bikes aka Boris Bikes / BoJo Bikes; previously Barclays Bikes and now Santander Bikes – bloomin heck this is complicated. I knew the direction where I wanted to get to and knew how to get there except I didn’t really.
We started our journey at one of bike hire station in St John’s Wood and continued around Regents Park (the wrong way) Simon got us to one of the gateways to the path along the Regents Canal. Sometimes they can be quite difficult to find; this was one of those times. This particular one was located at the back of a housing estate; only that Simon knew where it was there was no way I would have found it.
Story Text: "We took to the streets of London on the London hire bikes aka Boris Bikes / BoJo Bikes; previously Barclays Bikes and now Santander Bikes – bloomin heck this is complicated. I knew the direction where I wanted to get to and knew how to get there except I didn’t really.
We started our journey at one of bike hire station in St John’s Wood and continued around Regents Park (the wrong way) Simon got us to one of the gateways to the path along the Regents Canal. Sometimes they can be quite difficult to find; this was one of those times. This particular one was located at the back of a housing estate; only that Simon knew where it was there was no way I would have found it.
Off down the canal we went. Sunday afternoons are a busy time along the canal with local people mixed in with tourists from all over the world; so cycling along a narrow path is not easy as everyone walks on different sides of the path (according to where they come from)! We got towards Camden Market and the path got very busy, to the point that I almost went into the canal but with a wibble and a wobble I managed to stay in. At that point the decision was easily made to get off that bike and walk it. The Santander App showed us where the nearest parking station was and that there was space available to park up.
Coffee time! Forget the major chains, we found a small local place called T & G for some cups of coffee and a sarnie before we went out to find out next bike to get us to Granary Square in Kings Cross for our next stop. From the canal path there is a grassed set of steps going up to the Square but first we parked up the bikes on the other side of the canal. So many places to choose from to hang out, for drinks and for food or trains to Paris, Lille, Edinburgh or Manchester to start off with."
Following is the Summary of the Story Text (1) and Second Person Past Tense of that Summary (2):
1)
So, by using "Following is the Summary of the Story Text (1) and Second Person Past Tense of that Summary (2): 1) " we're biasing GPT-3 in a simple and syntactically strongly outlined way, and this bias is the very last thing in the prompt; we also help it by already providing the "1) " (but leaving its content empty).
GPT-3's zero-temperature result (model text-davinci-002):
We took the London hire bikes for a ride and ended up at a coffee shop near Camden Market.
2) You took the London hire bikes for a ride and ended up at a coffee shop near Camden Market.
I suggest you also add "3)" as stop sequence in case GPT-3 adds too much. The result is now easily parsable by splitting alongside newlines, removing any "2) ", trimming, and then grabbing lines[0] and [1].
Take a simple example: if a dog wants to eat all the food in different locations and there are some obstacles in the search path. Then how can I find a heuristic function in the A star algorithm that can reduce the expanded nodes?
I tried several heuristic functions, but they expanded too many nodes. So I wonder what is the best heuristic you can come up with?
Thanks
I coded a program in which people rate different products. Per rating people get a bonus point. The more bonus points people get the more reputation they get. But my issue that people sometimes give ratings not to rate but just to earn bonus points. Is there a mathematical solution to identify fake raters?
Absolutely. Search for "shilling recommender systems" in Google Scholar or elsewhere. There has been a decent amount of scholarly work identifying bad actors in recommender systems. Generally there's a focus on preventing robot actions (which doesn't seem to be your concern) as well as finding humans who rate differently than the norm (i.e., rating distributions, time-of-rating distributions).
https://scholar.google.com/scholar?hl=en&q=shilling+recommender+systems
I was playing around with P.Liang's brown clustering code link. To give it a try, I induced clusters on the text of "pride and prejudice". The clusters that I got were not so good. Some examples,
"further agreeable attempting pleasing reference"
"exempt identification deductible Service returns"
"impertinence amazement amusing"
"addresses astonished openly insincere conceit impertinent"
Do I need to perform some preprocessing(like removing stopwords, lemmatizing) before inducing the clusters?
In my experience you get much better clusters if you use larger values of k. You can then use the path prefixes to trim down the number of clusters.
ybisk has a good suggestion, when I tried to replicate your experiment I had better results with larger clusters. Some clusters are difficult to interpret, but there were a few with clear patterns, like this one for relations:
own dear sister father mother friend sister, uncle sisters aunt
sister's daughter manners mother, former father, brother aunt,
daughters mother's dear, friends spirits cousin daughter, husband
Catherine, brother, sister. own, father's feelings, friend, ladyship
eldest thoughts friend. Catherine's sisters, side, marriage, opinion,
friends, acquaintance, daughters, dearest wife, daughter. vanity
cousin,
lemmatizing and removing punctuation/capitalization would probably improve the clusters (I'm noticing a lot of duplicate words with trailing commas/periods in my results). I'm not sure removing stopwords would help, they could contain useful contextual information (e.g. day names will appear near words like "on" more often).
I have a pair of geo coordinates I don't understand. Does anybody know this coordinate system and how to translate them to longitude and latitude in degrees?
Long. 7662.251 West, Lat. 9144.590 North
Has to be a position in Honduras (13- 16°N and 83-89° W)
You really need to investigate the source of the data, files, print outs, local gandolphs etc. Even if someone can find something with a stab in the dark it's likely to be wrong in some tiny detail. Where did the pair of coordinates come from? A text file? An email? Were there any auxiliary files? With the same file name, but different extension? In the same folder? Attached to the email? Who gave it to you? What sort of computer do they use? Are they alive? Etc.
There's simply not enough information in this question to answer it - tell us at least how you know that they are "geo coordinates" - how do you know that they must be in the Honduras?
Unfortunately this level of cultural metadata is quite often about the best we have. As a massive stab in the dark they might be projected coordinates in km, rather than metres - but they could be in feet - or anything, there's really only educated guesses that can improve on this as it stands.
If it's not one of the common ones, such as UTM, have a search on the epsg registry for coordinate systems for Honduras:
http://www.epsg-registry.org/
and see if any work out. Would be a lot easier if you have any more coordinate pairs that are also in Honduras, because then we can work out an approximate scale.
I'll have a play and see if one of the UTM zones works out...