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A survey of data-driven and knowledge-aware explainable AI
We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …
Improving intrinsic exploration with language abstractions
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.
One common solution is to use intrinsic rewards to encourage agents to explore their …
One common solution is to use intrinsic rewards to encourage agents to explore their …
Learning with latent language
Deep reinforcement learning for cooperative robots based on adaptive sentiment feedback
Human–robot cooperative tasks have gained importance with the emergence of robotics
and artificial intelligence technology. In interactive reinforcement learning techniques, robots …
and artificial intelligence technology. In interactive reinforcement learning techniques, robots …
Vlm agents generate their own memories: Distilling experience into embodied programs of thought
Large-scale generative language and vision-language models (LLMs and VLMs) excel in
few-shot in-context learning for decision making and instruction following. However, they …
few-shot in-context learning for decision making and instruction following. However, they …
Nnetscape navigator: Complex demonstrations for web agents without a demonstrator
We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely
through synthetic demonstrations. These demonstrations are collected by first interacting …
through synthetic demonstrations. These demonstrations are collected by first interacting …
Learning to act with affordance-aware multimodal neural slam
Recent years have witnessed an emerging paradigm shift toward embodied artificial
intelligence, in which an agent must learn to solve challenging tasks by interacting with its …
intelligence, in which an agent must learn to solve challenging tasks by interacting with its …
Vlm agents generate their own memories: Distilling experience into embodied programs
Large-scale generative language and vision-language models excel in in-context learning
for decision making. However, they require high-quality exemplar demonstrations to be …
for decision making. However, they require high-quality exemplar demonstrations to be …