Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments

被引：3
|
作者：
Kolonin, Anton [1 ,2 ,3 ]
机构：
[1] Aigents, Novosibirsk, Russia
[2] SingularityNET Fdn, Amsterdam, Netherlands
[3] Novosibirsk State Univ, Novosibirsk, Russia
来源：
ARTIFICIAL GENERAL INTELLIGENCE, AGI 2021 | 2022年 / 13154卷
关键词：
Artificial general intelligence; Cognitive architecture; Domain ontology; Experiential learning; Global feedback; Local feedback; Neurosymbolic integration; Operational space; Reinforcement learning;
D O I：
10.1007/978-3-030-93758-4_12
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents a "horizontal neuro-symbolic integration" approach for artificial general intelligence along with elementary representation-agnostic cognitive architecture and explores its usability under the experiential learning framework for reinforcement learning problem powered by "global feedback".
引用
收藏
页码：106 / 115
页数：10
相关论文
共 39 条
  • [31] Instructional design based on Discrete-event simulation: provide a humanized and complete experiential learning cycle
    Liang, Hui-Yu
    Liu, Fei-Yan
    IEEE TALE2021: IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND EDUCATION, 2021, : 883 - 889
  • [32] Benchmarking Deep and Non-deep Reinforcement Learning Algorithms for Discrete Environments
    Duarte, Fernando F.
    Lau, Nuno
    Pereira, Artur
    Reis, Luis P.
    FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT 2019, VOL 2, 2020, 1093 : 263 - 275
  • [33] The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements
    Omslandseter, Rebekka Olsson
    Jiao, Lei
    Zhang, Xuan
    Yazidi, Anis
    Oommen, B. John
    AI 2021: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, 13151 : 507 - 518
  • [34] Evaluating experiential learning in the business context: contributions to group-based and cross-functional working
    Piercy, Niall
    INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL, 2013, 50 (02) : 202 - 213
  • [35] Case Study in Experiential Learning - From Chaos to Order: Sensemaking with the Interactive Timeline Tool in Architecture and Civil Engineering Studies
    Nutt, Nele
    Salmistu, Sirle
    Meitl, Cassi
    Karu, Katrin
    EDUCATING ENGINEERS FOR FUTURE INDUSTRIAL REVOLUTIONS, ICL2020, VOL 1, 2021, 1328 : 91 - 102
  • [36] Modeling Production Scheduling Problems as Reinforcement Learning Environments based on Discrete-Event Simulation and OpenAl Gym
    Lang, Sebastian
    Kuetgens, Maximilian
    Reichardt, Paul
    Reggelin, Tobias
    IFAC PAPERSONLINE, 2021, 54 (01): : 793 - 798
  • [37] Service Selection for Service-Oriented Architecture using Off-line Reinforcement Learning in Dynamic Environments
    Kondo, Yuya
    Moustafa, Ahmed
    ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 1, 2022, : 64 - 70
  • [38] Respective advantages and disadvantages of model-based and model-free reinforcement learning in a robotics neuro-inspired cognitive architecture
    Renaudo, Erwan
    Girard, Benoit
    Chatila, Raja
    Khamassi, Mehdi
    6TH ANNUAL INTERNATIONAL CONFERENCE ON BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES (BICA 2015), 2015, 71 : 178 - 184
  • [39] Integration of functional resonance analysis method and reinforcement learning for updating and optimizing emergency procedures in variable environments
    Liu, Xuan
    Meng, Huixing
    An, Xu
    Xing, Jinduo
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 241
1234