Re: Simulated natural language dialogue

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
>>
>>
>>
>>
>>