Summary of Towards Goal-oriented Agents For Evolving Problems Observed Via Conversation, by Michael Free et al.
Towards Goal-Oriented Agents for Evolving Problems Observed via Conversation
by Michael Free, Andrew Langworthy, Mary Dimitropoulaki, Simon Thompson
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to train chatbots that can solve complex problems through conversations with users. The proposed system consists of a virtual problem, a simulated user, and a Deep Q-Network (DQN)-based chatbot architecture. The chatbot is trained using reinforcement learning to solve the problem through dialogue with the simulated user. The authors explore different training methods, including curriculum learning, and analyze the effect of modified reward functions on model performance as the environment complexity increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of this paper is to develop a chatbot that can solve evolving problems by conversing with users about a problem it cannot directly observe. To achieve this, the authors propose an architecture combining a virtual problem, a simulated user, and a DQN-based chatbot. The chatbot learns through dialogue with the simulated user using reinforcement learning. The paper explores different training methods and their impact on model performance as the environment complexity increases. |
Keywords
* Artificial intelligence * Curriculum learning * Reinforcement learning
