15 citations · 35 across the 6 of their papers we have counts for
6 papers
Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis
James R. Kirk, Robert E. Wray, Peter Lindes +1
Large language models (LLMs) offer significant promise as a knowledge source for task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LL…
Computational-level Analysis of Constraint Compliance for General Intelligence
Robert E. Wray, Steven J. Jones, John E. Laird
Human behavior is conditioned by codes and norms that constrain action. Rules, ``manners,'' laws, and moral imperatives are examples of classes of constraints that govern human beh…
Improving Language Model Prompting in Support of Semi-autonomous Task Learning
James R. Kirk, Robert E. Wray, Peter Lindes +1
Language models (LLMs) offer potential as a source of knowledge for agents that need to acquire new task competencies within a performance environment. We describe efforts toward a…
Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks
James R. Kirk, Robert E. Wray, Peter Lindes +1
Autonomous agents are able to draw on a wide variety of potential sources of task knowledge; however current approaches invariably focus on only one or two. Here we investigate the…
Language Models as a Knowledge Source for Cognitive Agents
Robert E. Wray, III, James R. Kirk +1
Language models (LMs) are sentence-completion engines trained on massive corpora. LMs have emerged as a significant breakthrough in natural-language processing, providing capabilit…
An Architectural Approach to Ensuring Consistency in Hierarchical Execution
J. E. Laird, R. E. Wray
Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of…