Presentation information

Organized Session

Organized Session » OS-10

[1N4-OS-10a] System1型+2型統合AIへの展望(1/2)

Tue. Jun 14, 2022 2:20 PM - 4:00 PM Room N (Room 501)

オーガナイザ:栗原 聡(慶應義塾大学)[現地]、山川 宏(全脳アーキテクチャ・イニシアティブ)、三宅 陽一郎(スクウェア・エニックス)

2:20 PM - 2:40 PM

[1N4-OS-10a-01] Automatic Parameter Tuning Based on Agent Similarity in Activity Propagation Multi-Agent Planning

〇Daiki Shimokawa1, Naoto Yoshida1, Shuzo Koyama1, Satoshi Kurihara2 (1. Graduate School of Science and Technology, Keio University, 2. Faculty of Science and Technology, Keio University)

Keywords:AI, Planning, Evolutionary Computation

Currently, there has been a lot of research on so-called Narrow AI, but the study of Artificial General Intelligence (AGI) is still in the beginning phase. For the realization of AGI, the planning method that integrates System1 and System2 is necessary. Therefore, the application of Activity Propagation Multi-Agent Planning has been proposed as a method that combines both immediate and deliberative planning for this purpose. However, the parameter tuning method required to run this network has not been established, and a huge number of parameters have been determined manually. Therefore, in this paper, we attempted to automate the parameter adjustment using evolutionary computation, taking into account the similarity of Agent. The experiments were conducted in a simulation environment called Tile World. The results show that by using NeuroEvolution of Augmenting Topologies (NEAT), an evolutionary computation, we were able to obtain a high degree of fitness under the experimental environment and automatically adjust the parameters considering the similarity of Agent.

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