Integrating Conversational Case Retrieval with generative Planning

Some problem-solving tasks are amenable to integrated case retrieval and generative planning techniques. This is certainly true for some decision support tasks, in which a user controls the problem-solving process but cannot provide a complete domain theory. Unfortunately, existing integrations are either non-interactive or require a complete domain theory and/or complete world state to produce acceptable plans, preventing them from being easily used in these situations. We describe a novel integrated algorithm, named SiN, that is interactive and does not require a complete domain theory or complete world state. SiN users leverage a conversational case retriever to focus both partial world state acquisition and plan generation. We highlight the benefits of SiN (e.g., quadratically fewer cases needed) in an experimental study using a new travel planning domain.