Interactive Transfer of Expertise: Acquisition of New Inference Rules

By Randall Davis. 1993

Subfields: Representation & Self-Supervised Learning

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In Representation & Self-Supervised Learning

Citations in the corpus

Memory-assisted prompt editing to improve GPT-3 after deployment (2022, background): “While system debugging through dialogue has been explored in many contexts (Hixon et al., 2015; Wang et al., 2016; Davis, 1977), our novel contribution is a dialogue about the model’s understanding of the user’s intent.”
Learning Knowledge Graphs for Question Answering through Conversational Dialog (2015, background): “TEIRESIAS (Davis, 1977) reﬁnes inference rules from terse interaction with experts.”
Explaining and justifying the advice of a decision support system: a natural language generation approach (2003, background): “Teiresias (Davis, 1979) is a system that uses meta-level knowledge in knowledge-acquisition applications.”
Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge (2020, background): “A longstanding goal of artificial intelligence is to develop systems that continuously accumulate knowledge by consuming facts and rules about the world and reasoning over them [1, 2, 3].”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “Work that enables the communication between human experts and machines include expert systems [30, 26, 28, 29, 47, 98, 42] and a subset of interactive machine learning systems [92].”
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge (2020, background): “A longstanding goal of artiﬁcial intelligence is to develop systems that continuously accumulate knowledge by consuming facts and rules about the world and reasoning over them [1, 2, 3].”