Search Games with Predictions
security
Study explores search games with mobile Searcher and immobile Hider, considering consistency and robustness tradeoffs in search strategies.
Search Games with Predictions
Major Takeaways
- Search games involve a Searcher trying to locate a Hider in an environment, with the Searcher aiming to minimize some payoff, such as the time to find the Hider or a normalized search time.
- This study presents a new setting where the Searcher has potentially erroneous information or predictions on the Hider’s position, leading to tradeoffs between consistency and robustness of search strategies.
- The paper explores optimal consistency/robustness tradeoffs for three fundamental search games, including searching in discrete locations, expanding search in a tree network, and linear search in an infinite line.
Introduction
- Search games are a common task in everyday life, with applications in various fields such as search-and-rescue operations and robotics.
- These games have been studied under the mathematical formulation of a zero-sum two-person game, with a focus on identifying the value of the search game and applying it to real-world problems.
Search Games with Predictions
- This study introduces a new approach where the Searcher has predictions about the Hider’s location, leading to a tradeoff between consistency and robustness of search strategies.
- The objective is to find the Pareto frontier of the game, describing the best-possible consistency under a given robustness value or the best-possible robustness under a given consistency value.
Contribution
- The paper studies three important search games under the predictions model: searching in discrete locations, expanding search in a tree network, and linear search in an infinite line.
- It provides Pareto-optimal strategies that achieve optimal consistency-robustness tradeoffs, particularly for randomized algorithms, filling a gap in the analysis of such tradeoffs.
Preliminaries
- The paper introduces the consistency and robustness metrics for search strategies with predictions, aiming to minimize both metrics to find the Pareto frontier.
- It uses the concept of scalarization from multiobjective optimization to characterize the Pareto frontier of the game.
Box Search
- The study explores a fundamental search game where a Hider hides in one of a set of boxes, and a Searcher looks in the boxes one by one until finding the target.
- It presents Pareto-optimal strategies and characterizes the Pareto frontier for box search with predictions.
Expanding Search on a Tree Network
- This section extends the model to expanding search on a tree network and demonstrates the Pareto-optimal strategies for this scenario under the predictions model.
A General Approach to Characterizing the Pareto Frontier
- The paper presents a general approach for finding the Pareto frontier of search games, applying it to arbitrary two-player zero-sum games.
Searching on the Infinite Line
- The study expands the analysis to the linear search problem, focusing specifically on finding Pareto-optimal strategies with predictions for the Searcher’s location.
Conclusion
- The paper concludes by emphasizing its pioneering analysis of search games with predictions and suggests potential applications of this framework in other classes of games rooted in Search Theory, such as patrolling, rendezvous, and cops and robbers games.
Critique
The paper provides a comprehensive and insightful analysis of search games with predictions. However, it could benefit from clearer explanations of the implications of the findings in practical scenarios and potential limitations of the proposed framework. Additionally, further empirical validation of the proposed strategies in real-world search scenarios could enhance the paper’s practical relevance.
Appendix
| Model | gpt-3.5-turbo-1106 |
| Date Generated | 2024-02-26 |
| Abstract | http://arxiv.org/abs/2401.01149v1 |
| HTML | https://browse.arxiv.org/html/2401.01149v1 |
| Truncated | False |
| Word Count | 8210 |