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"Like I was saying Honest John ..." Threads Of Conversation Continuity In My Chatbot

If you have been following my chatbot articles, you will know that I have been on a mission to develop an artificially intelligent chatbot that will replace a car salesman. This idea came to me after friends of mine had a bad experience at a new car shop. Building a simple chatbot was quite easy. I fired up my SDK (Software Development Kit) and had one running within a couple of days.

I used the AIML (Artificial Intelligence MarkUp Language) as a starting point, and after I got it working, I realized that the thing (I call it a he, and his name is Honest John) needed more smarts. But on top of that, Honest John needed to detect emotions in the human on the other side of the silicon. The reason for this, is that I wanted a successful conclusion (a sale) from the interactions with the customer. If the customer was getting frustrated or irate, Honest John needed to know. He would tone down his stance and be less hard-nosed when bargaining. The ultimate aim, is not to get the last nickel on the table for the car dealer, but to satisfy both the buyer and the seller and to come to a successful commercial conclusion.

In my last article I talked about my emotion detector framework. It is a learning framework where the customer would help Honest John by clicking on an emoji every once in awhile when asked if Honest John couldn't get a read. From there, the emotion detector framework remembers the AIML predicate (the key word or word pattern that identifies the intent and meaning of the input) and couples it to the emoji, the words in the input, the counter offer in negotiating, the delta or difference in the bid and ask of Honest John and the customer, the number of words in the replies and feeds it into a neural network to continuously learn from its experiences. It then updates its strategy processes based on a decision tree. As a negotiator, Honest John will ultimately know when he needs the kid gloves or when he needs to play hardball to sell the car to the satisfaction of the buyer AND the dealer.

But as I was coding this, I realized that there was one thing missing -- the conversation continuity thread for Honest John. The buyer on the other side of the screen can see the dialog history and it is in the buyers memory, but not in Honest John`s memory. The dialog history is stored in the database, but it is no help to the bot to have to do a fetch after every interaction. The fix was easy. One needs a Conversation Continuity Object in memory.

When you build and enterprise web-based platform, say in Java, you have session objects that are stored in memory. A typical session object is a user bean that holds everything that is needed about the user, so that you don`t have to keep making trips to the database every time you want to personalize a message. The net result of this session object, is that Honest John will now have total recall of the conversation in memory.

The Conversation Continuity Object will not only record the transcript, but it will also have the metadata and analytics and it will create and update the process maps for both successful and unsuccessful sales. The real advantage is that Honest John will have some cognition about the whole process instead of just reacting to the latest input, like most chatbots do.

The strategic and intelligent factor, is that Honest John will be able to reset. He can go back to an earlier point and start over without having to re-do or re-learn the whole conversation. That will be the trait that could make Honest John a real winner in the marketplace, to sell not only cars, but pretty much anything that need negotiating.

The next key to making a super smart negotiating chatbot, is developing strategies for Honest John and having them available, extensible and modifiable. More on that and the psychology behind it in a later article.

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