Summary of “a Good Bot Always Knows Its Limitations”: Assessing Autonomous System Decision-making Competencies Through Factorized Machine Self-confidence, by Brett Israelsen et al.
“A Good Bot Always Knows Its Limitations”: Assessing Autonomous System Decision-making Competencies through Factorized Machine Self-confidence
by Brett Israelsen, Nisar R. Ahmed, Matthew Aitken, Eric W. Frew, Dale A. Lawrence, Brian M. Argrow
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Factorized Machine Self-confidence (FaMSeC) framework is a computational approach that enables autonomous systems to assess their competencies in completing tasks. By using self-assessments based on knowledge about the world, itself, and its ability to reason and execute tasks, FaMSeC provides a holistic description of factors driving algorithmic decision-making processes. These factors include outcome assessment, solver quality, model quality, alignment quality, and past experience. The framework derives self-confidence indicators from hierarchical problem-solving statistics embedded within probabilistic decision-making algorithms like Markov decision processes. This approach allows for algorithmic goodness-of-fit evaluations to be incorporated into the design of autonomous agents through human-interpretable competency self-assessment reports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous machines need a way to know how good they are at doing tasks. This paper shows how they can do this by looking at their own performance and comparing it to what’s expected. The idea is that these machines can think about what they’re good at, like solving problems or making decisions, and then use that information to figure out how confident they should be in themselves. This helps the machines make better decisions and work better with humans. |
Keywords
» Artificial intelligence » Alignment
