Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. Hence, in this paper, we are proposing another form of social learning, Learning from a Conversation (LfC). LfC is an open-ended machine learning system in which an artificially intelligent agent learns from extended dialog with a human. Our system enables the agent to adapt to new changes based on the human input. We provide a detailed description of our system and report its performance by providing several examples that reflect our system's efficiency. Test results indicate that the prototype was successful in learning from conversation.

In many cases, creating long-term solutions to sustainability issues requires not only innovative technology, but also large-scale public adoption of the proposed solutions. Social simulations are a valuable but underutilized tool that can help public policy researchers understand when sustainable practices are likely to make the delicate transition from being an individual choice to becoming a social norm. In this paper, we introduce a new normative multi-agent architecture, Cognitive Social Learners (CSL), that models bottom-up norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance. CSL preserves a greater sense of cognitive realism than influence propagation or infectious transmission approaches, enabling the modeling of complex beliefs and contradictory objectives within an agent-based simulation. In this paper, we demonstrate the use of CSL for modeling norm emergence of recycling practices and public participation in a smoke-free campus initiative.