Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems

By Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, Lin Guan. 2021

56 citations recorded by the source corpus.

Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as (system-produced) abstractions use in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. Symbols, like emotions, may well not be sine qua non for intelligence per se, but they will be crucial for AI systems to interact with us humans--as we can neither turn off our emotions not get by without our symbols. In particular, in many human-designed domains, humans would be interested in providing explicit (symbolic) knowledge and advice--and expect machine explanations in kind. This alone requires AI systems to at least do their I/O in symbolic terms. In this blue sky paper, we argue this point of view, and discuss research directions that need to be pursued to allow for this type of human-AI interaction.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lin Guan
2. Ruohan Zhang
3. Mudit Verma
4. Subbarao Kambhampati
5. Sihang Guo
6. Sarath Sreedharan

In Computer Vision and Pattern Recognition

1. Lin Guan
2. Subbarao Kambhampati
3. Sihang Guo
4. Ruohan Zhang

Citations in the corpus

• Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation (2020, background): “…of relevance to them, and automatically converting those to saliency regions, is an instance of the general approach for explainable and advisable AI systems that use symbols as a lingua franca for communicating with humans, independent of whether they use symbolic reasoning internally [13].”