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Risk Assessment of Reinforcement Learning AI Systems Looking Beyond the Technology / Kyle Bunch.
- Format:
- Book
- Author/Creator:
- Bunch, Kyle, author.
- Hou, Alexander C., author.
- Haberman, Ryan, author.
- Herron, Marissa, author.
- Jacques, Anthony, author.
- Briggs, Gary J., author.
- Series:
- Research report (Rand Corporation) ; A1473-1.
- Report ; A1473-1
- Language:
- English
- Subjects (All):
- United States. Department of Defense--Technological innovations.
- United States.
- Artificial intelligence--Military applications--United States.
- Artificial intelligence.
- Reinforcement learning.
- Machine learning.
- Risk assessment.
- Autonomous Military Systems.
- Machine Learning.
- United States Department of Defense.
- risk assessment.
- Local Subjects:
- Autonomous Military Systems.
- Machine Learning.
- United States Department of Defense.
- Physical Description:
- xi, 88 pages : illustrations ; 26 cm.
- Other Title:
- Risk Assessment of Reinforcement Learning Artificial Intelligence Systems
- Place of Publication:
- Santa Monica, CA : RAND, [2024]
- Summary:
- This report presents some of the challenges that the U.S. Department of Defense (DoD) may face in fielding an artificial intelligence (AI) technology called reinforcement learning (RL) in DoD applications. RL has been credited with expanding the decisionmaking ability of machines beyond that of humans in playing complex games of strategy. The fact that RL-enabled systems can beat world experts in these games raises the question of whether such systems could outperform humans in DoD applications. Especially relevant are "broad" applications having large, complex processes with multiple steps leading to few but consequential decisions for a military commander. Timely alternatives could lead to decisive advantages in such situations. What is not clear, however, is what risks such a system would introduce from a technical standpoint (i.e., technical failure leading to mission failure) or the risks to the force structure incurred in absorbing such technology. This report represents a first step toward understanding such risks associated with employing RL-enabled systems for operational-level command and control.
- Contents:
- CHAPTER 1: Narrow Versus Broad Artificial Intelligence and an Overview of Reinforcement Learning
- CHAPTER 2: Further Technical Challenges of RL in Broader Problems
- CHAPTER 3: Scenarios for Exploring Challenges
- CHAPTER 4: Challenge Identification Framework and Workshop
- CHAPTER 5: Conclusions and Recommendations
- APPENDIX A: Additional Workshop Details
- APPENDIX B: Challenges, Implications, and Mitigation Strategies Identified in the Workshop.
- Notes:
- Title from PDF document (title page; viewed July 3, 2024)
- "Prepared for Office of the Under Secretary of Defense for Research and Engineering"
- "RAND NATIONAL DEFENSE RESEARCH INSTITUTE"
- Includes bibliographical references (pages 79-88)
- ISBN:
- 1977413153
- 9781977413154
- OCLC:
- 1444074996
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