I am trying to do some planing project by using PDDL. I want to do from some natural text. Like:
Take the knife from left to right hand.
Cut the apple.
Wash knife.
I am confused abut the predicates and action. how to defined the predicates?
Predicates are the things that can be true or false in a state of the environment. E.g., (holding ?hand ?item).
Actions would be the things that change the world. E.g., take, cut, wash.
Related
I cannot get my head around what HTN is. What I believe is that we want to decompose all the small actions of a plan into a higher level plan.
For example:
"Take bus" is an HLA of i.e. "go to bus", "buy ticket", "sit down" etc.
Am I wrong? Would appreciate a simple explanation.
I know this is an old post, but I bashed my head against this wall for a long time and hope what I learned will save someone else the time.
In broad strokes, an HTN solves a problem using a very human planning approach that centers on abstraction and alternatives.
Alternatives: Let’s use a travel example from the famous SHOP HTN Planner (http://www.cs.umd.edu/projects/shop/description.html). Imagine you asked me how to get from downtown to the park. I might say, “Well, it is 2Km away but, if it is nice out, I would just walk it. However, if it is raining and you’ve got taxi fare, I’d take the taxi. Otherwise, if you have bus fare, just take a bus.” A abstract thing you accomplish like “travelling from one place to another” is known in HTNs as a “Task”. The 3 alternatives I gave for accomplishing it are known as “Methods” in HTNs. Note that each Method had a “Condition” that went with it: walk if it is nice out, take a taxi if you have the fare, etc.
Abstraction: Note that these 3 Methods are at a pretty high level of abstraction. You might further ask me how to accomplish the “TakeABus” Method. I’d say something like, “Well, wait for the #1 bus at the ‘Downtown’ bus stop, pay the driver $1, and ride it to the ‘Park’ stop.” These three tasks are known in HTNs as “Subtasks” of the “TakeABus” Method. In HTNs, Methods do what they do by decomposing into Subtasks. Now, this is a pretty complete answer for a human but maybe not if we were talking to a robot. In that case we might need to further decompose the task of “PayTheDriver$1” into something like “Reach into your pocket, grab a $1 bill, hand to driver”. Each of those could further be decomposed until we finally have something a robot could do. The final step of an HTN, the part that actually gets done, is called an “Operator”. How detailed you need to get before you are done (and have an Operator) depends on the kind of system you are building. A big part of designing an HTN model is getting your head around what the right abstractions are.
So, HTNs have these parts:
State of the world: You are downtown. Taxi fare is $2. Bus fare is $1. Distance from downtown to park is 2Km.
Tasks: What can be done abstractly in the world (there may be several ways to do it!): Travel from A to B. Walk from A to B. Pay a Driver X$. Wait for bus A at stop B.
Operators: How to accomplish abstract Tasks when they can “just be done” without further explanation. They actually modify the State of the world. If you are human these might be Operators: Pay the driver $1, Ride #1 bus to the 'Park' stop. If you are a robot, these might be: Move stepper motor forward by 100 steps, turn on spotlight
Methods: How to accomplish abstract Tasks when they involve several steps or more detailed plans. They have a Condition for when they can be used and a set of Subtasks for how to accomplish them: METHOD: "Travel from A to B" CONDITION: the weather is bad and you have bus fare, SUBTASKS: Wait for the bus at the bus stop, pay the driver, and ride it to the stop
The algorithm itself is pretty simple. You start with a Task or Goal list like Go from downtown to the park and an empty Plan list:
Remove and start with the first goal in the Goal List
See if there is an Operator that knows how to do it. If so, add the Operator to the end of the Plan List (since Operators can “just be done”) and continue at #1
See if there are Methods that know how to do it. If so, add their Subtasks to the front of our Goal List and continue at #1 If there are multiple Methods, duplicate the current state of the world and run the algorithm with each, independently. Each that completes successfully will be a different solution
Repeat until the Goal List is empty
When done, what you have in the Plan List is the solution
I've got a blog series that goes over this in pretty good detail including source code and more examples if you want more detail: https://blog.inductorsoftware.com/blog/htnoverview
Firstly, a hierarchical task network is a parser for a given plan. In the example, the plan:
1 takebus
1.1 gotobus
1.2 buyticket
1.3 sitdown
can be tested against the HTN-network and it will answer the request with: “plan is valid” and “it took 30 minutes to execute”. Another plan which is modified a bit, can also be executed against the HTN-network.
What the planning community around the ICAPS conference is doing, is figuring out, how to build hierarchical task networks. They are often using the Prolog-dialect, PDDL, for creating complex behavior trees. For example, to parse plans about the dock workers robot problem or to parse plans from the blocks world domain.
Secondly, the term hierarchical task network planning is also used if the aim is to find the optimal plan for a given domain. Here we have already the PDDL description and we're interested in finding the shortest plan. This is useful in control-tasks mostly with the aim of autonomous game-playing.
Overall, it is correct to say, that hierarchical task networks are a fundamental building block of every cognitive architecture. It combines disciplines like game AI programming, knowledge engineering and heuristic planning.
In Salesforce's Service Cloud one can enable the out of the box search function where the user enters a term and the system searches all parts of the database for a match. I would like to enable smart searching of acronyms so that if I spell an organizations name the search functionality will also search for associated acronyms in the database. For example, if I search type in American Automobile Association, I would also get results that contain both "American Automobile Association" and "AAA".
I imagine such a script would involve declaring that if the term being searched contains one or more spaces or periods, take the first letter of the first word and concatenate it with the letters that follow subsequent spaces or periods.
I have unsuccessfully tried to find scripts for this or articles on enabling this functionality in Salesforce. Any guidance would be appreciated.
Interesting question! I don't think there's a straightforward answer but as it's standard search functionality, not 100% programming related - you might want to cross-post it to salesforce.stackexchange.com
Let's start with searchable fields list: https://help.salesforce.com/articleView?id=search_fields_business_accounts.htm&type=0
In Setup there's standard functionality for Synonyms, quite easy to use. It's not a silver bullet though, applies only to certain objects like Knowledge Base (if you use it). Still - it claims to work on Cases too so if there's "AAA" in Case description it should still be good enough?
You could also check out the trick with marking a text field as indexed and/or external ID and adding there all your variations / acronyms: https://success.salesforce.com/ideaView?id=08730000000H6m2 This is more work, to prepare / sanitize your data upfront but it's not a bad idea.
Similar idea would be to use Tags although that could explode in size very quickly. It's ridiculous to create a tag for every single company.
You can do some really smart things in data deduplication rules. Too much to write it all here, check out the trailhead: https://trailhead.salesforce.com/en/modules/sales_admin_duplicate_management/units/sales_admin_duplicate_management_unit_2 No idea if it impacts search though.
If you suffer from bad address data there are State & Country picklists, no more mess with CA / California / SoCal... https://resources.docs.salesforce.com/204/latest/en-us/sfdc/pdf/state_country_picklists_impl_guide.pdf Might not help with Name problem...
Data.com cleanup might help. Paid service I think, no idea if it affects search too. But if enabling it can bring these common abbreviations into your org - might be better than reinventing the wheel.
What I am trying to do is identify the context of the query a user might input. So if the user enters "High Proteins", I want to be able to understand that what he means by that is "protein > certain_threshold".
Example 2: User input : "Calories less than 250"
I should be able to understand that what the user means by this is calories < 250
If I am able to do this, I will be able to construct my queries accordingly. Which sub-topic of NLP will help me do this. Any leads woul be greatly appreciated.
You probably do not need NLP if you do not have rich vocabulary. You might just want to use simple dictionaries or regex to specify your queries, just as in a controlled language.
If indeed you need more than this, as you have a very rich vocabulary and complex syntactic relations between your phrases, you should probably start with part-of-speech tagging, chunking, and then maybe parsing. But I wouldn't go that way unless you specifically need to.
One way to see this is as a very simple programming language.
You have a set of special key words you want to look for like "Calories" or "Protein" or "LDL" and some operations you want to do like (keyword > 3000) or maybe (%RDA keyword < 2%) that need to be matched. Usually you can do this with a simple expression grammar parser. Depending on what kinds of things you want to connect together (like do you want to say AND or OR or NOT or UNLESS, etc) , and what programming language you want to do this with, it may even be available from a standard library like MARPA (multiple languages) or ANTLR (Java) or Nearly (JavaScript).
Some of the words you might search for include "Earley Parser" or "Context Free Parser" or "Expression Parser". You probably don't need to write your own code, just leverage what already exists.
Basically my decision tree can't classify a value using the normal algorithm.
I get to a node, and there are two options (say, sunny and windy), but at this node my value is different (for example, rainy).
Are there any methods to deal with this, e.g. change the tree or just estimate based on other data?
I was thinking of assigning the most common value at that node but this is just a guess.
Have you considered fuzzy logic for the rich/poor continuum? As for things that can't be expressed as a continuum, I can't think of a way it can be done. Rainy weather, for example, is so fundamentally different from sunny and windy weather in how we experience and react to it, I'm not sure how you expect a computer (or whatever it is you're writing your decision tree for) to figure out what to do. (Aside from simply having an "I don't know what to do" output state, but I'm assuming you wanted something more meaningful than that.)
The whole point in decision trees is that the options are complete and (hopefully) mutual exclusive.
If it is not you'll get into trouble. Redefine poor and rich to cover everything. (all incomes, all states of mind...)
But honestly, interpret such weather examples as what they are: just examples for a concept, not the holy grail of meteorology.
The issue here is that you've learned a decision from different data as you are using to classify it. More specific, your decision tree knows only two values (i.e., sunny and windy) for the attribute Weather. But your data for classification also allows the value rainy.
Since your decision tree has no observation when the weather was rainy, this value turns useless. In other words, you have to eliminate this value from your classification.
The only solution is to do data cleaning before using the decision tree as classifier.
You have two options:
1. Remove all observations/instances with Weather="rainy" from your data set because you can't classify them. The disadvantage is that all instances with Weather="rainy" are not classified.
2. For all observations/instances with Weather="rainy", remove the value or rather set it to unknown/null. In case that your decision tree can handle null values, it can classify all of your data set. If not, you still have a problem. In that case you should go for option 3.
3. Relearn your decision tree with Weather={sunny, windy, rainy}
(4). In your case the following is not an option. Replace "rainy" with either "sunny" or "rainy. There are different heuristics for that.
You are talking about the "normal algorithm", which is a quite blurry statement. I assume you are using a strictly-binary rooted decision tree, where the each internal node makes a binary split of the data. Thus, the condition evaluation at each internal node outputs a Boolean variable, which splits the data into the left node (true) and right node (false). In your case, you can have a categorical variable weather with two possible values in the training data, which makes only two possible node: weather==sunny or weather==windy. Hence, the rainy samples will be always on the right node, as it is not sunny and not windy.
In the following picture, the rainy samples will be classified as not sunny, not windy.
I'm writing a bridge between the user and a search engine, not a search engine. Part of my value added will be inferring the intent of a query. The intent of a tracking number, stock symbol, or address is fairly obvious. If I can categorise a query, then I can decide if the user even needs to see search results. Of course, if I cannot, then they will see search results. I am currently designing this inference engine.
I'm writing a parser; it should take any given token and assign it a category. Here are some theoretical English examples:
"denver" is a USCITY and a PLACENAME
"aapl" is a NASDAQSYMBOL and a STOCKTICKERSYMBOL
"555 555 5555" is a USPHONENUMBER
I know that each of these cases will most likely require specific handling, however I'm not sure where to start.
Ideally I'd end up with something simple like:
queryCategory = magicCategoryFinder( query )
>print queryCategory
>"SOMECATEGORY or a list"
Natural language parsing is a complicated topic. One of the problems here is that determining what a word is depends on context and implied knowledge. Also, you're not so much interested in words as you are in groups of words. Consider, "New York City" is a place but its three words, two of which (new and city) have other meanings.
also you have to consider ambiguity, which is once again where context and implied knowledge comes in. For example, JAVA is (or was) a stock symbol for Sun Microsystems. It's also a programming language, a place and has meaning associated with coffee. How do you classify it? You'd need to know the context in which it was used.
And if you can solve that problem reliably you can make yourself very wealthy.
What's all this in aid of anyway?
To learn about "tagging" (the term of art for what you're trying to do), I suggest playing around with NLTK's tag module. More generally, NLTK, the Natural Language ToolKit, is an excellent toolkit (based on the Python programming language) for experimentation and learning in the field of Natural Language Processing (whether it's suitable for a given production application may be a different issue, esp. if said application requires very high speed processing on large volumes of data -- but, you have to walk before you can run!-).
You're bumping up against one of the hardest problems in computer science today... determining semantics from english context. This is the classic text mining problem and get into some very advanced topics. I thiink I would suggest thinking more about you're problem and see if you can a) go without categorization or b) perhaps utilize structural info such as document position or something to give you a hint (is either a city or placename or an undetermined) and maybe some lookup tables to help. ie stock symbols are pretty easy to create a pretty full lookup for. You might consider downloading CIA world factbook for a lookup of cities... etc.
As others have already pointed out, this is an exceptionally difficult task. The classic test is a pair of sentences:Time flies like an arrow.Fruit flies like a bananna.
In the first sentence, "flies" is a verb. In the second, it's part of a noun. In the first, "like" is an adverb, but in the second it's a verb. The context doesn't make this particularly easy to sort out either -- there's no obvious difference between "Time" and "Fruit" (both normally nouns). Likewise, "arrow" and "bananna" are both normally nouns.
It can be done -- but it really is decidedly non-trivial.
Although it might not help you much with disambiguation, you could use Cyc. It's a huge database of what things are that's intended to be used in AI applications (though I haven't heard any success stories).