Abstract
Human social learning is an effective process that has inspired many existing machine learning approaches, such as learning from observation and learning by demonstration. In this paper, we introduce another form of social learning, learning from a casual conversation or LCC a machine learning approach in which an artificially intelligent agent learns new information through an extended natural language dialog with a human. Our system enables the agent to add or change information in its knowledge base as a result of the human’s conversational text inputs. LCC seeks to close an important gap in the state of the art that has focused on teaching computer agents how to perform specific tasks. Furthermore, LCC could also provide an efficient way to enhance the knowledge base of certain types of systems without requiring the involvement of a programmer. LCC does not require the user to enter specific information; instead, the user can converse naturally with the agent. As part of its learning process, LCC identifies the text inputs from the conversing human that contain information worth learning, and then determines whether the inputs are heretofore unknown and learns it; in agreement with what it already “knows” and ignores it; or in conflict with what it “knows” and it must resolve the conflict. LCC’s architecture consists of multiple sub-systems combined to perform the above tasks. Its learning component can add new information to the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. The LCC system functionality was rigorously assessed with test statements comprising various difficulty levels. Furthermore, its acceptance by human users was evaluated by two separate groups of human test subjects—one group who interacted with the system, and a second group that evaluated the logs of the interactions of the first group. The collected results were all found to be acceptable and within the range of our expectations.
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All used data can be found in the appendices of the first author full dissertation document that can be accessed from https://stars.library.ucf.edu
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Not applicable at the moment but the authors are planning to publish the code soon
Notes
Behavioral cloning is a type of imitation learning where the agent receives the states and actions of an expert demonstrator as training data then the learning agent uses a supervised machine learning approach such as a classifier to replicate the demonstrator policy (Torabi et al., 2018) It is very similar in nature to LfO and LfD.
TF-IDF stands for Term Frequency-Inverse Document Frequency, a numerical measure that reflects the importance of a word in a document or corpus.
SemEval is an ongoing series of evaluations of computational semantic analysis systems. The evaluation involves exploring the natural meaning of the language. This task is not intuitive to machines as it is for humans (International workshop on semantic evaluation, 2015).
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Work was supported indirectly through a teaching assistantship from the University of Central Florida for the first author.
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AMA: Conceptualization, methodology; investigation; software development: data acquisition and curation; writing and editing. AJG: conceptualization; project administration; supervision: manuscript editing
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The use of human test subjects and surveys were approved by the Institutional Review Board at the University of Central Florida, SBE-18-1418 dated: 8/14/2018. The approval letter can be found in APPENDIX H from the frist author dissertation document that can be accessed from https://stars.library.ucf.edu/etd/6297/. The authors consent that the submitted work is original and have not have been published or submitted elsewhere.
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Mohammed Ali, A.E., Gonzalez, A.J. Machine learning from casual conversation. Mach Learn 112, 4789–4836 (2023). https://doi.org/10.1007/s10994-023-06383-0
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DOI: https://doi.org/10.1007/s10994-023-06383-0