no code implementations • 21 Sep 2023 • Levon Haroutunian, Zhuang Li, Lucian Galescu, Philip Cohen, Raj Tumuluri, Gholamreza Haffari
Our approach involves initially generating a set of candidate outputs by prompting an LLM and subsequently reranking them using a task-specific reranker model.
no code implementations • 19 Feb 2023 • Philip R. Cohen, Lucian Galescu
The system does this by inferring users' intentions and plans to achieve those goals, detects whether obstacles are present, finds plans to overcome them or to achieve higher-level goals, and plans its actions, including speech acts, to help users accomplish those goals.
no code implementations • WS 2018 • Lucian Galescu, Choh Man Teng, James Allen, Ian Perera
We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains.
no code implementations • WS 2018 • Ian Perera, James Allen, Choh Man Teng, Lucian Galescu
We present a modular, end-to-end dialogue system for a situated agent to address a multimodal, natural language dialogue task in which the agent learns complex representations of block structure classes through assertions, demonstrations, and questioning.
no code implementations • WS 2018 • Ian Perera, James Allen, Choh Man Teng, Lucian Galescu
We demonstrate a system for understanding natural language utterances for structure description and placement in a situated blocks world context.