Scalable Interactive Machine Learning for Future Command and Control
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Abstract (arXiv)
Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robust decision-making processes and decision-support tools, integration of artificial and human intelligence holds the potential to revolutionize the C2 operations process to ensure adaptability and efficiency in rapidly changing operational environments. We propose to leverage recent promising breakthroughs in interactive machine learning, in which humans can cooperate with machine learning algorithms to guide machine learning algorithm behavior. This paper identifies several gaps in state-of-the-art science and technology that future work should address to extend these approaches to function in complex C2 contexts. In particular, we describe three research focus areas that together, aim to enable scalable interactive machine learning (SIML): 1) developing human-AI interaction algorithms to enable planning in complex, dynamic situations; 2) fostering resilient human-AI teams through optimizing roles, configurations, and trust; and 3) scaling algorithms and human-AI teams for flexibility across a range of potential contexts and situations.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 67 of its 138 references that are in the map. First posted on arXiv 9 Feb 2024.
Builds on
67 of 138 references.
References · 67
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2020Learning to summarize from human feedbackcited by 2,562
- 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
- 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
- 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
38 more
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
- 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 915
- 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
- 2018Data-Efficient Hierarchical Reinforcement Learningcited by 436
- 2016The Option-Critic Architecturecited by 433
- 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
- 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 328
- 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 298
- 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
- 2021How Many Data Points is a Prompt Worth?cited by 197
- 2023Guiding Pretraining in Reinforcement Learning with Large Language Modelscited by 185
- 2019A Survey of Reinforcement Learning Informed by Natural Languagecited by 154
- 2017Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problemscited by 126
- 2023Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHFcited by 125
- 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
- 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
- 2016Deep Learning for Identifying Metastatic Breast Cancercited by 93
- 2017Meta Learning Shared Hierarchiescited by 93
- 2016A Deep Hierarchical Approach to Lifelong Learning in Minecraftcited by 90
- 2022A very preliminary analysis of DALL-E 2cited by 82
- 2017Coordinated Multi-Agent Imitation Learningcited by 70
- 2022Correcting Robot Plans with Natural Language Feedbackcited by 68
- 2018Modeling Others using Oneself in Multi-Agent Reinforcement Learningcited by 64
- 2014Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcingcited by 63
- 2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
- 2018A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processescited by 52
- 2022Imitation Learning by Estimating Expertise of Demonstratorscited by 50
- 2019Leveraging Human Guidance for Deep Reinforcement Learning Taskscited by 42
- 2021Perfection Not Required? Human-AI Partnerships in Code Translationcited by 42
- 2019Learning Human Objectives by Evaluating Hypothetical Behaviorcited by 41
- 2018Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimizationcited by 40
- 2021Hierarchical Skills for Efficient Explorationcited by 37
- 2021Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedbackcited by 34
- 2023DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarioscited by 33
- 2022A Survey of Explainable Reinforcement Learningcited by 31
- 2021Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rolloutscited by 24
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