4 research outputs found
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Our work aims at efficiently leveraging ambiguous demonstrations for the
training of a reinforcement learning (RL) agent. An ambiguous demonstration can
usually be interpreted in multiple ways, which severely hinders the RL-Agent
from learning stably and efficiently. Since an optimal demonstration may also
suffer from being ambiguous, previous works that combine RL and learning from
demonstration (RLfD works) may not work well. Inspired by how humans handle
such situations, we propose to use self-explanation (an agent generates
explanations for itself) to recognize valuable high-level relational features
as an interpretation of why a successful trajectory is successful. This way,
the agent can provide some guidance for its RL learning. Our main contribution
is to propose the Self-Explanation for RL from Demonstrations (SERLfD)
framework, which can overcome the limitations of traditional RLfD works. Our
experimental results show that an RLfD model can be improved by using our
SERLfD framework in terms of training stability and performance
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
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