Explainable Human-AI Interaction: A Planning Perspective

• URL: http://arxiv.org/abs/2405.15804v1
• Date: Sun, 19 May 2024 22:22:21 GMT
• Title: Explainable Human-AI Interaction: A Planning Perspective
• Authors: Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati,
• Abstract summary: AI systems need to be explainable to the humans in the loop. We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
• Score: 32.477369282996385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: From its inception, AI has had a rather ambivalent relationship with humans -- swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human-AI interaction is that the AI systems be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. Drawing from several years of research in our lab, we will discuss how the AI agent can use these mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception. Although the book is primarily driven by our own research in these areas, in every chapter, we will provide ample connections to relevant research from other groups.

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