Stochastic Planning in Large Search Spaces
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4000-4000.
Abstract
Multi-agent planning approaches are employed for many problems including task allocation, surveillance and video games. In the first part of my thesis, we study two multi-robot planning problems, i.e. patrolling and task allocation. For the patrolling problem, we present a novel stochastic search technique, Monte Carlo Tree Search with Useful Cycles, that can generate optimal cyclic patrol policies with theoretical convergence guarantees. For the multi-robot task allocation problem, we propose an Monte Carlo Tree Search based satisficing method using branch and bound paradigm along with a novel search parallelization technique. In the second part of my thesis, we develop a stochastic multi-agent narrative planner employing MCTS along with new heuristic and pruning methods applicable for other planning domains as well.
@inproceedings{ijcai2016p574,
title = {Stochastic Planning in Large Search Spaces},
author = {Kartal, Bilal},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4000--4000},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/574},
}
TY - CPAPER TI - Stochastic Planning in Large Search Spaces AU - Kartal, Bilal T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4000 EP - 4000 UR - https://ijcai.org/proceedings/2016/574 ER -Copied
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