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A simple pythonic chatbot themed as KITT from Knightrider
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Project KITT - Version 2

Ross Mason, August 2019

Project Aim

Develop a conversational Chatbot in Python with the personality of KITT from the Knight Rider (1982–1986) Television Series.

This project was not intended to construct a program able pass the Turing test - or win the Loebner Prize, but to inspire the authors appreciation of the design and engineering challenges when creating a conversational UI.

Part of Series: 2/2

KITT Version 1 - KITT's Basic Input/Output functions, responding to keywords from pre-determined lists of responses.
KITT Version 2 - Supplementing with some basic NLP functionality (imported as blackbox from another programmer).

What can KITT Version 2 do, in addition to Version 1 features?

  • Use the TextBlob NLP software (A Python Library sitting on top of the more comphrensive and open source Natural Language Tool Kit), to construct more meaningful responses.
  • Use the Class created in Version 1 to provide non-random list cycling responses.
  • Converse in a meaningful way that is somewhat understandable.

How does KITT Version 2 do it?

  • KITT Version 2 is based on a limited version of Brobot an example Bot created by Liza Daly to demonstrate using NLP in a Conversational UI.

KITT Version 2 uses Textblob to tokenise, parse and catorgorise the input sentences into grammatical components, (noun, pronoun, verb, adjective), and applies a series of heirarchical rules, dictating how to respond, most of which end with a choice of random responses from a list, some of which try to reflect some of the users input back to the user.

KITT Version 2 - Is a combination of Brobot code and my KITT Version 1's code bolted together in an inelegant way.

  • Defined Functions & External TextBlob library.


A little lost at the start of this project, as looking for something that can enhance programming knowledge with an AI focus, but not too big. The NLTK and working through the guidance on this does not meet that criteria.

A Skim through all the chapters of the the NLTK Tutorial Book has been informative me on two fronts, firstly it is a detailed guide, not just for using NLP techniques in python with the massively helpful methods in the NLTK library, but also as an intermediate Pythonic tutorial itself, with a highly comprehensive refresher of Pythonic knowledge interweaved in the book.

However, once again the breadth and depth of NLP is a subject worthy of immense study of itself, and although working through the many excercises in this book would be a fantastic start, time is limited.

In order to get the functionality I am looking for into KITT, I may have to use the example code and simpler NLP library (Textblob - which sits on the shoulders of the NLTK), by copying and hacking it from the Chatbot creation guide by Liza, however, I will only be able to produce an output, I won't understand how or why - perhaps this is the sacrifice I need to make to get started?

Went back to Liza Daly's tutorial, looked at her code in order to reverse engineer something and gain some understanding of the coded implementaton if not how/why it works.

Bolted on my user input functionality from Kitt Version 1 - But have not included the randomised response function, as would mean too many changes to code that I have not touched to maintain functionality of the NLP parsing. (Basically, I don't know how Textblob or the imported logic works well enough to add my feature, and spending time on this aspect takes me further away from the core aim.

A lot of learning gained and recorded into Jupyter Notebooks when coding this project, at least 20+ discrete writeups/lookups and additions to my learning notebooks. An absolute smasher in terms of learning experience.

Project Files

  • Contains the listed responses - Imported in main program
  • Details of hacking through original brobot code for comprehsion and enhancing functionality.
  • List of Banned Words for filtering user input and Bot output.
  • Main Program - runs from CMD Line or Interpreter.
  • Notes and Configuration.

Detailed Notes

  • For raw working notes and planning for the project, please see associated Jupyter Notebook in the "Learning Python" Section of this repository.
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