Household Management AI Dissertation

Title:

Efficiency-Driven Household AI: Proactive Planning and Commonsense Integration for Seamless chores.

Project Aim:

The project’s goal is to create an intelligent household agent that can efficiently manage the day-to-day chores in the house by anticipatory planning, logical reasoning, and commonsense priors. The agent would be able to predict the sequential steps of the tasks and prioritise actions based on user preference.

Significance:

The project aims to reduce the workload in domestic environment by offloading day to day tasks which reduces the valuable time of individuals. The project outcome is beneficial for people with reduced mobility by promoting greater independence in household tasks. The integration of anticipatory efficiency, logical reasoning, and commonsense knowledge empowers the assistant to provide proactive and personalized assistance, enhancing the overall user experience.

Relevance:

By developing an intelligent household assistant, the project aims to simplify and optimize daily chores, providing user their personal time and reduced stress. The integration of anticipatory efficiency and logical reasoning streamlines task execution, enhancing overall household productivity. With commonsense knowledge integration, the assistant becomes adaptable to real-world scenarios, making it a reliable and trustworthy companion in various living situations.

Project Objective:

1. Develop an intelligent household assistant capable of anticipating and planning task sequences efficiently, minimizing redundant actions and optimizing time management.
2. Integrate logical reasoning and prioritize tasks based on time constraint and user preferences.
3. Incorporate a comprehensive commonsense knowledge prior to handle real-world scenarios.
4. Implement seamless execution of a wide range of household tasks, including cleaning, cooking, organization, and more, with accuracy and efficiency.

Measurement of Accuracy:

1. Task Anticipation Accuracy: Measure the accuracy of the assistant's predictions for the next steps in task sequences. Compare the predicted actions with the actual user instructions to assess the assistant's anticipatory efficiency.
2. Task Planning Optimization: Assistant's ability to plan task sequences logically and prioritize actions based on time constraint and user preferences. Measure the reduction in time wastage and the efficiency gained through task optimization.
3. Commonsense Knowledge Handling: Assess the assistant's performance in handling real- world scenarios. Measure the accuracy of its responses and decisions in novel situations.
4. Task Execution Accuracy: Monitor the assistant's ability to accurately execute household tasks, such as cleaning, cooking, and organization, based on user instructions and anticipatory planning.

System architecture:

1. Language model: This forms the core of the system, providing natural language processing capabilities and allowing users to interact with the assistant conveniently.
2. Anticipation Module: The system incorporates an anticipation mechanism that predicts the next steps in a task sequence, enabling proactive planning.
3. Planning Engine: Using AI planning techniques, the assistant devises efficient task sequences based on the anticipated actions, optimizing task execution.
4. Commonsense Knowledge base: To handle scenarios not explicitly present in the training data, a custom commonsense knowledge base is integrated into the system.

Methodology:

Gathering a diverse dataset of household tasks including various scenarios like cleaning, cooking and organisation, and other daily chores. Train a language model (GPT – 2/3.5) on the pre-processed data to enable natural language understanding and generation. Develop an anticipatory module that utilizes the trained language model to predict the sequence of task.

Implementing logical reasoning capabilities to handle task dependencies and prioritisation of actions. Integrate a commonsense knowledge base to augment the language model’s understanding of real-world scenarios. Develop task execution algorithm that translate user’s instruction into specified actions of the agent.

Benchmark Comparison:

Compare the performance of the agent with the baseline models (existing household management system) to compare accuracy and performance.

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