k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems

Brown, Daniel S.; Loscalzo, Steven; Gemelli, Nathaniel

doi:10.1007/978-3-319-23114-3_11

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also provide polynomial‐time algorithms for solving one‐dimensional variants where the sites are located along a path for both the single and multiagent cases but never evaluate these algorithms on any actual problem instances. Brown et al () examine the problem of determining how many agents are required for multiagent stochastic physical search problems. Our work extends and enhances a previous work by providing exact solvers for the single‐agent min‐budget and max‐probability variants on complete metric graphs.…”

Section: Related Workmentioning

confidence: 99%

Exact and Heuristic Algorithms for Risk‐Aware Stochastic Physical Search

Brown

Hudack

Gemelli

et al. 2016

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk-aware stochastic physical search (RA-SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task-planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location may only be revealed when an agent physically arrives at the location, and the agent may need to use a single resource to both search for and acquire the item of interest. We present exact and heuristic algorithms for solving RA-SPS problems on complete metric graphs. We first formulate the problem as mixed integer linear programming problem. We then develop custom branch and bound algorithms that result in a dramatic reduction in computation time. Using these algorithms, we generate empirical insights into the hardness landscape of the RA-SPS problem and compare the performance of several heuristics.

show abstract

Section: Related Workmentioning

confidence: 99%

Exact and Heuristic Algorithms for Risk‐Aware Stochastic Physical Search

Brown

Hudack

Gemelli

et al. 2016

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, [6] presented an MILP formulation and a branch-and-bound optimal algorithm for general graphs, which work only if the graph is complete. In a different paper, [7] investigate the minimal number of agents required to solve Max-Probability and Min-Budget problems on a path and in a 2-dimensional Euclidean space.…”

Section: Related Workmentioning

confidence: 99%

Approximation and Heuristic Algorithms for Probabilistic Physical Search on General Graphs

Hazon,

Gonen,

Kleb

2015

Preprint

View full text Add to dashboard Cite

We consider an agent seeking to obtain an item, potentially available at different locations in a physical environment. The traveling costs between locations are known in advance, but there is only probabilistic knowledge regarding the possible prices of the item at any given location. Given such a setting, the problem is to find a plan that maximizes the probability of acquiring the good while minimizing both travel and purchase costs. Sample applications include agents in search-and-rescue or exploration missions, e.g., a rover on Mars seeking to mine a specific mineral. These probabilistic physical search problems have been previously studied, but we present the first approximation and heuristic algorithms for solving such problems on general graphs. We establish an interesting connection between these problems and classical graph-search problems, which led us to provide the approximation algorithms and hardness of approximation results for our settings. We further suggest several heuristics for practical use, and demonstrate their effectiveness with simulation on real graph structure and synthetic graphs.

show abstract

Probabilistic physical search on general graphs: approximations and heuristics

Hazon

Gonen

2019

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems

Cited by 3 publications

References 9 publications

Exact and Heuristic Algorithms for Risk‐Aware Stochastic Physical Search

Exact and Heuristic Algorithms for Risk‐Aware Stochastic Physical Search

Approximation and Heuristic Algorithms for Probabilistic Physical Search on General Graphs

Probabilistic physical search on general graphs: approximations and heuristics

Contact Info

Product

Resources

About