[1]MA Chengyu,LIU Huaping,GE Quanbo.Scene-aware decentralized Monte Carlo Tree Search of target discovery[J].CAAI Transactions on Intelligent Systems,2022,17(6):1244-1253.[doi:10.11992/tis.202110012]
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Scene-aware decentralized Monte Carlo Tree Search of target discovery

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1244-1253 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-11-05
Title:
Scene-aware decentralized Monte Carlo Tree Search of target discovery
Author(s):
MA Chengyu1;  LIU Huaping2;  GE Quanbo3; 4
1. School of Instrumentation and Electrical Engineering, Nantong University, Nantong 226019, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
4. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Keywords:
scene graph; decentralization; target search; Monte Carlo Tree Search; multi-objective optimization; action planning; multi-agent system; visual semantic navigation
CLC:
TP391
DOI:
10.11992/tis.202110012
Abstract:
In visual semantic navigation, agents find and navigate to the target of a given object category through visual information. However, the majority of existing studies complete the task using a learning-based framework. These studies have a high training cost in the real world, low portability, and are only suitable for single-agent systems with low efficiency and poor fault tolerance. To address the above issues, this paper suggests a decentralized multi-objective optimization Monte Carlo Tree Search model based on scene awareness. In this model, multi-agent plans online in real time and does not need to train in advance. The improved Monte Carlo Tree Search is used for path planning to search targets, and the environment is estimated in real-time by using scene awareness prior knowledge combined with observation information. Experiments in the Matterport3D dataset demonstrate that the effectiveness of the model is significantly higher than that of a single agent.
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