Towards Goal-Oriented Agents for Evolving Problems Observed via Conversation

• URL: http://arxiv.org/abs/2401.05822v1
• Date: Thu, 11 Jan 2024 10:38:43 GMT
• Title: Towards Goal-Oriented Agents for Evolving Problems Observed via Conversation
• Authors: Michael Free, Andrew Langworthy, Mary Dimitropoulaki, Simon Thompson
• Abstract summary: The objective of this work is to train a chatbots capable of solving evolving problems through conversing with a user. The system consists of a virtual problem (in this case a simple game), a simulated user capable of answering natural language questions that can observe and perform actions on the problem, and a Deep Q-Network (DQN)-based chatbots architecture.
• Score: 1.33134751838052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The objective of this work is to train a chatbot capable of solving evolving problems through conversing with a user about a problem the chatbot cannot directly observe. The system consists of a virtual problem (in this case a simple game), a simulated user capable of answering natural language questions that can observe and perform actions on the problem, and a Deep Q-Network (DQN)-based chatbot architecture. The chatbot is trained with the goal of solving the problem through dialogue with the simulated user using reinforcement learning. The contributions of this paper are as follows: a proposed architecture to apply a conversational DQN-based agent to evolving problems, an exploration of training methods such as curriculum learning on model performance and the effect of modified reward functions in the case of increasing environment complexity.

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