TY - GEN
T1 - Probabilistic physical search on general graphs
T2 - 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
AU - Hazon, Noam
AU - Gonen, Mira
N1 - Publisher Copyright:
© 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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 a real graph structure.
AB - 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 a real graph structure.
KW - Graph search
KW - Planning under uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85096652565&partnerID=8YFLogxK
U2 - 10.1007/s10458-019-09423-z
DO - 10.1007/s10458-019-09423-z
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85096652565
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 2143
EP - 2145
BT - Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
A2 - An, Bo
A2 - El Fallah Seghrouchni, Amal
A2 - Sukthankar, Gita
Y2 - 9 May 2020 through 13 May 2020
ER -