Agents that Converse

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This research project aims to bridge the gap between task-specific AI and conversational AI models. On one hand, task-specific AI models have reached super-human ability in many areas such as logistics planning, microchip layout optimization, games and various control problems, but remain largely black-box models, with little avenue for obtaining explanations or collaboratively refining proposed solutions. On the other hand, conversational AI models naturally interface with humans through conversations, but usually lack superhuman capability in the goal oriented behaviours that task specific AIs excel at.Through this combination of conversational AI and task-specific AI I aim to engage with some of the following questions. How can we use natural language to make requests from learnt agents acting in an environment? How can we train an agent acting in an environment to be able to provide justifications or explanations for their actions in natural language? How can we improve the performance of an agent learning from experiential data by supplementing it with domain-related natural language data?To do this I will explore avenues for making conversational AI agents more adept at goal oriented behaviour, even in tasks that are not natural language-based in nature through avenues that utilise what we already know about training performant task-specific AI. One approach would be through jointly training a task-specific AI model with a conversational model in a way that the representations they learn about the world are compatible. Other approaches involve starting with a powerful pretrained conversational model and fine-tuning it to excel at a task that is usually performed by task-specific AI. For the latter kind of approach, research is needed in order to learn how to do this while retaining the model's conversational abilities. Because, in general, training models to learn new skills tends to make them forget previously learnt skills.Through training these models we will be able to obtain models that are more interpretable and collaborative. This is becoming increasingly more important in an age where AI models are increasingly used in making decisions that affect human lives. Making these decisions more interpretable is necessary for making overall processes that utilise AI-decision-making fairer. Furthermore, making these AIs conversational will change the paradigm from one where a model makes a single final indisputable decision, to one where people can work collaboratively with a model until a final decision is reached.This project falls within the EPSRC AI and Robotics research area and is conducted under the supervision of Professor Jakob Foerster