Re: Simulated natural language dialogue

Let me add

looks like you are trying to reproduce an intelligent conversation between
two humans
(nice job!) but based on my experience, there are many things that can go
wrong with that approach
in a real-world situation that the system would not be  able to handle
(unless it's a very expensive system that can handle any situation)

while in my example I try to complete a set of tasks with the least amount
of effort and minimum risk of going wrong
so I think I understand why you are doing it that way, but I think I would
model the exchange differently
to reduce uncertainty hence effort required to get the task accomplished

P


On Sat, Jul 18, 2020 at 10:51 AM Paola Di Maio <paola.dimaio@gmail.com>
wrote:

> So - from the KR point of view, I would simply use very informative lists
> of options for each task
> T= task
> A= agent
>
> T1 - GREETINGS (where the variables are a time of the day and is only
> sir/madam etc)
> A1/A2 (Agent)
>
> T2- A1 offer of service (list of options)
> T2.1 choose table,
> T2.2. order food ,
> T2.3 order drink
> T2.4 anything else (information about toilet, phone, other info)
>
>
> T3 -  A2 requests more information (thats the tricky bit - A2 the customer
> would want to know
> if the mushrooms are fresh or frozen, if the fish comes from sustainable
> sources, what is the percentage of blueberry in the jam etc)
>
> Ideally, the KR for T3 is carried in such a way that all the possible
> information about the food and drink choice is available to the
> the customer before they order
> so the menu would list the foods and drinks and also have a page/narrative
> associated with them
> that would list exclusions (dont eat this if you are allergic to.....)
> and calorie count etc etc
>
> If you think this would be useful I can write it up more clearly
>
> PDM
>
>
> On Sat, Jul 18, 2020 at 10:31 AM Paola Di Maio <paola.dimaio@gmail.com>
> wrote:
>
>> Thank you Dave
>> Neat stuff!
>>
>> good example of how a simple human interaction needs a lot of thinking
>> and planning to be reproduced-
>> I ll be interested in the implementation, is there going to be a demo?
>>
>> My approach is a bit different, in the sense that I would never attempt
>> to reproduce a human level conversation
>> (which you do well in your example) and I would expect that a
>> conversational agent would be implemented in a highly digitized environment
>> where there is not need to tell the customer that the table x is not
>> available and that the dish y is not available
>> because in a digital environment this information would be updated in the
>> system
>>
>> I ll be more narrow to get (probably) the same result (the table and the
>> food ordered) with less thinking
>> for example, I d go more about
>>
>> waiter - welcome, what can I do for you?  //maybe provide a list of
>> options, like order now, reserve for later or  after order service follow
>> up on a an earlier order such as enquire about lost and found items or a
>> credit card charge etc)
>> customer - order dinner/meal, please
>> waiter- here or takeaway?
>> customer - here
>> waiter - please choose your table from those available (from a table plan
>> /map)
>> /I would assume the customer figures out that there is no available table
>> near the window if it is not on the available seats plan which is updated
>> every time a customer arrives/leaves//
>> waiter-  here is the menu
>> //I would assume if an item is not available /off it would not be on the
>> menu!! which is digitally updated every minute//
>> etc etc
>>
>> I would also want a button that says çall me the human please always
>> flashing
>>
>> So the bottom line of my comment here is that we develop automated agents
>> thinking of a nonautomated deployment environment
>> I think thats a bit of a general flaw
>>
>> PDM
>>
>>
>>
>> On Fri, Jul 17, 2020 at 9:20 PM Dave Raggett <dsr@w3.org> wrote:
>>
>>> Natural language will be key to future human-machine collaboration, as
>>> well as to being able to teach everyday skills to cognitive agents. There
>>> are many potential market opportunities, and many challenges to overcome.
>>>
>>> I previously developed a simple demo for natural language parsing based
>>> around the Towers of Hanoi game. This demo uses very simple language, and
>>> allows you to type or speak the command to move discs between pegs. The
>>> demo uses a shift-reduce parser with the parse tree represented in chunks.
>>>
>>> https://www.w3.org/Data/demos/chunks/nlp/toh/
>>>
>>> I am now working on a more ambitious demo featuring a dialogue between a
>>> waiter and a customer dining at a restaurant. The idea is to have a single
>>> web page emulate the waiter and customer as separate cognitive agents, and
>>> for each agent to apply natural language generation and understanding as
>>> they each take turns to speak and listen to each other. The text they speak
>>> will be shown with chat bubbles in a manner familiar from smart phone chat
>>> services. The demo scenario was chosen as the language usage, the semantics
>>> and pragmatics are well understood and limited in scope.
>>>
>>> The aim is to support word by word incremental concurrent processing of
>>> syntax and semantics without backtracking. This selects the most
>>> appropriate meaning given the preceding words, the dialogue history and
>>> other knowledge through the application of rules and graph algorithms,
>>> including spreading activation. This process works in reverse for natural
>>> language generation.
>>>
>>> My starting point has been to define a dinner plan as a sequence of
>>> stages (greetings, find table, read menu, place order, …), where each stage
>>> links to the following stage. I’ve represented the utterances as a sequence
>>> of chunks, where each utterance links to the previous utterance, and to the
>>> associated stage in the plan. This has involved a commitment to a small set
>>> of speech acts, e.g. greeting, farewell, assertion, question, and answer,
>>> along with positive and negative acknowledgements that are associated with
>>> additional information.
>>>
>>> Along the way, I am evolving a means to represent the parse trees for
>>> utterances as linked chunks, and will next work on the semantics and
>>> pragmatics for polite discourse.  I also want to explore how to use the
>>> statistics in natural language understanding (competence) for natural
>>> language generation (performance). You can follow my progress  on the
>>> following page:
>>>
>>> https://github.com/w3c/cogai/blob/master/demos/nlp/dinner/README.md
>>>
>>> Note: you will need to click the bottom of the section on knowledge
>>> representation to view the chunk representation of the utterances including
>>> the parse trees.
>>>
>>> If anyone would like to help with this work, including offering
>>> guidance, please get in touch!
>>>
>>> Many thanks,
>>>
>>> Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
>>> W3C Data Activity Lead & W3C champion for the Web of things
>>>
>>>
>>>
>>>
>>>