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Existing artificial intelligence (AI) agents are most successful on narrow, well-defined tasks, where training data are plentiful, well-labeled, and match the deployment scenarios. It is also possible to train an AI agent to do multiple tasks (such as identifying IEDs from image data as well as recognizing faces), given sufficient time to craft the training regime and network architecture. However, data and time are often in short supply -- multi-domain operations involve rapidly shifting and adaptive compositions of capabilities, against adversaries that will likely be adapting on the fly. We suggest that to be robust in such scenarios, AIs need to be capable of learning in the field with opportunistically-available data, limited human oversight, and limited or no access to ground truth. These challenges also apply to reinforcement learning agents, with the additional challenge that for such agents, bad decisions cascade and pose a further difficulty in learning new tasks on the fly. We present an overview of the challenges in enabling AI systems for multi-domain operations, current algorithmic approaches for developing lifelong learning agents, and potential techniques for evaluating them.
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