Reflective Linguistic Programming (RLP): A Stepping Stone in Socially-Aware AGI (SocialAGI)

Autor:	Fischer, Kevin A.
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Human-Computer Interaction Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper presents Reflective Linguistic Programming (RLP), a unique approach to conversational AI that emphasizes self-awareness and strategic planning. RLP encourages models to introspect on their own predefined personality traits, emotional responses to incoming messages, and planned strategies, enabling contextually rich, coherent, and engaging interactions. A striking illustration of RLP's potential involves a toy example, an AI persona with an adversarial orientation, a demon named `Bogus' inspired by the children's fairy tale Hansel & Gretel. Bogus exhibits sophisticated behaviors, such as strategic deception and sensitivity to user discomfort, that spontaneously arise from the model's introspection and strategic planning. These behaviors are not pre-programmed or prompted, but emerge as a result of the model's advanced cognitive modeling. The potential applications of RLP in socially-aware AGI (Social AGI) are vast, from nuanced negotiations and mental health support systems to the creation of diverse and dynamic AI personas. Our exploration of deception serves as a stepping stone towards a new frontier in AGI, one filled with opportunities for advanced cognitive modeling and the creation of truly human `digital souls'. Comment: 12 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.12647 Zobrazit plný text záznamu View this record from Arxiv