Intelligent Personal Assistant with Knowledge Navigation

TL;DR

This paper presents an intelligent personal assistant that leverages knowledge navigation, multi-modal input/output, and learning from human dialogues to provide accurate, human-like responses and efficient information retrieval.

Contribution

It introduces a novel knowledge navigation approach combined with multi-modal input/output and semi-supervised learning for an improved personal assistant.

Findings

The agent can respond using multiple input/output methods.

It learns from human dialogues for more natural responses.

Knowledge navigation enhances information retrieval accuracy.

Abstract

An Intelligent Personal Agent (IPA) is an agent that has the purpose of helping the user to gain information through reliable resources with the help of knowledge navigation techniques and saving time to search the best content. The agent is also responsible for responding to the chat-based queries with the help of Conversation Corpus. We will be testing different methods for optimal query generation. To felicitate the ease of usage of the application, the agent will be able to accept the input through Text (Keyboard), Voice (Speech Recognition) and Server (Facebook) and output responses using the same method. Existing chat bots reply by making changes in the input, but we will give responses based on multiple SRT files. The model will learn using the human dialogs dataset and will be able respond human-like. Responses to queries about famous things (places, people, and words) can be provided using web scraping which will enable the bot to have knowledge navigation features. The agent will even learn from its past experiences supporting semi-supervised learning.