There are a number of ways that AI can be used to address the issue of fragile agents that fail in unexpected ways. For example, AI can be used to develop better models of agent behavior that are more resilient to unexpected failures. AI can also be used to develop better methods for detecting and diagnosing failures in agents. Additionally, AI can be used to develop methods for repairing or replacing failed agents.
Each of these approaches has its own advantages and disadvantages that need to be considered when selecting an AI-based approach for addressing the issue of fragile agents.
Developing better models of agent behavior is a promising approach for making agents more resilient to unexpected failures. AI can help to develop such models by providing a means to learn from past failures and by developing models that can better anticipate and cope with future failures.
One advantage of this approach is that it has the potential to improve the overall robustness of agents by making them less likely to fail in unexpected ways. Additionally, this approach can help to make agents more able to recover from failures that do occur.
A disadvantage of this approach is that it requires a significant investment of time and resources in order to develop accurate models of agent behavior. Additionally, this approach may not be effective in cases where the causes of failures are not known or understood.
Developing better methods for detecting and diagnosing failures in agents is another promising approach for making agents more robust and reliable. AI can help to develop such methods by providing a means of automated monitoring and diagnostics. Additionally, AI can be used to develop methods for automatically repairing or replacing failed agents.
One advantage of this approach is that it can help to identify failures before they cause significant problems. Additionally, this approach can help to minimize the impact of failures that do occur by quickly repairing or replacing failed agents.
A disadvantage of this approach is that it requires a significant investment of time and resources in order to develop effective monitoring and diagnostic methods. Additionally, this approach may not be effective in cases where the causes of failures are not known or understood.
AI presents a number of opportunities for addressing the issue of fragile agents that fail in unexpected ways. By developing better models of agent behavior and better methods for detecting, diagnosing, and repairing failures, AI can help to make agents more robust and reliable.
References:
https://www.technologyreview.com/s/601474/the-promise-and-pitfalls-of-using-ai-to-design-better-robots/