My Robot Friend

A desktop rule-based chatbot proficient in identifying specific keywords within user inputs to generate tailored responses. So far the bot can identify several greetings, and respond to some specific queries.

Ask 'how are you?' or ask for it's name!

Used Python

Future Plans

Plans to extend My Robot Friend into a more advanced AI chatbot with NLP, tokenization, neural networks, and human-like qualities:

• Enhance Natural Language Processing (NLP):

  • Implement advanced NLP techniques to enable the chatbot to understand context, sentiment, and nuances in user input.
  • Incorporate named entity recognition and sentiment analysis to provide more context-aware and emotionally intelligent responses.

• Utilize Tokenization and Neural Networks:

  • Employ tokenization methods to break down text inputs into meaningful units, facilitating deeper understanding.
  • Integrate neural networks, such as recurrent neural networks (RNNs) or transformer models, to improve the chatbot's ability to generate coherent and contextually relevant responses.

• Implement Machine Learning Models:

  • Train the chatbot using machine learning models, allowing it to learn from user interactions and continuously improve its responses.
  • Explore reinforcement learning techniques to make the chatbot adapt and refine its conversational abilities over time.

• Develop Human-like Conversational Skills:

  • Focus on improving the chatbot's ability to hold natural and engaging conversations with users.
  • Incorporate dialogue generation models to ensure that responses sound more human-like and less robotic.

• Personalization and User Adaptation:

  • Implement algorithms for user profiling and personalization to tailor responses based on individual user preferences and historical interactions.
  • Enable the chatbot to remember past conversations and refer back to them for continuity and improved user engagement.

• Continuous Evaluation and Feedback Loop:

  • Establish a feedback mechanism to collect user feedback and use it to fine-tune the chatbot's responses and performance.
  • Regularly evaluate and benchmark the chatbot against human-like conversational benchmarks to track progress and set improvement goals.