Generic-to-Specific Reasoning and Learning for Scalable Ad Hoc Teamwork

Consider one or more AI agents performing daily living tasks in collaboration with a human they have not worked with before. Figure 1 shows snapshots of a motivating scenario in which two AI agents (male, blue shirt; female, red dress) and a human agent (female, green top) are preparing breakfast and setting up a workstation. The agents (AI, human) have a limited view of the environment and do not communicate with each other, although each of them is aware of the state of the domain, including the location of teammates and the outcomes of their actions (e.g., change in location of an object moved by a teammate). The AI agents have to reason with different descriptions of domain knowledge and uncertainty that include qualitative statements ("eggs are usually in the fridge") and quantitative measures of uncertainty ("I am 90% sure I saw the eggs on the kitchen table"), adapting their actions to changes in the domain and teammates' behavior. These characteristics correspond to Ad Hoc T eamwork (AHT), which requires cooperation "on the fly" without prior coordination [1]; many practical problems such as disaster rescue are AHT problems. The state of the art in AHT has moved from using preset protocols that define specific actions to be performed in specific states, to methods that use a long history of prior experiences to build a deep network model of the behavior of other agents (or agent types) and optimize the ad hoc agent's behavior [2]. However, it is difficult to gather large datasets of different situations in complex domains. Also, these methods are opaque and make it difficult to revise the existing models over time. In a departure from existing work, we design an architecture for AHT that bridges knowledge-based and data-driven reasoning and learning, enabling an ad hoc agent to: Leverage the ability of a Large Language Model (LLM) to anticipate future high-level tasks to be completed, revising and adapting the LLM's output to domain-specific knowledge and experience; Perform non-monotonic logical reasoning with prior commonsense domain knowledge at different abstractions, and learned models predicting the behavior of other agents, toward achieving current and anticipated tasks as joint goals; and Rapidly identify the need for, learn, and revise the models predicting the behavior of each teammate to facilitate scalable collaboration in complex domains.