In a recent paper introduced at the 2021 AAAI Conference on Artificial Intelligence (AAAI), we describe an AI that trades off ‘exploration’ of the world with ‘exploitation’ of its action strategy to maximize rewards. In Reinforcement Learning, an AI gets a reward – such as a bag of gold behind a locked door in a video game – every time it reaches specific desirable states. We have greatly improved this exploration vs exploitation tradeoff using additional commonsense knowledge – in the form of crowdsourced text. Our work could lead to better mapping and navigation applications, and to a new generation of interactive assistive agents able to reason like humans.
IEEE ICC 2019 “Best Paper” details novel deep reinforcement learning approach to maximize overall performance of Software-Defined Networking that supports 5G.
As AI-powered autonomous agents play an increasingly large role in society, we must ensure that their behavior aligns with societal values. To this end, we developed a novel technique for training an AI agent to operate optimally in a given environment while following implicit constraints on its behavior. Our strategy incorporates a bottom-up (or demonstration-based) […]
Recently, impressive progress has been made in neural network question answering (QA) systems which can analyze a passage to answer a question. These systems work by matching a representation of the question to the text to find the relevant answer phrase. But what if the text is potentially all of Wikipedia? And what if the […]