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
  • [1] Learning Where and When to Reason in Neuro-Symbolic Inference
    Cornelio, Cristina
    Stuehmer, Jan
    Hu, Shell Xu
    Hospedales, Timothy
    NEURAL-SYMBOLIC LEARNING AND REASONING 2023, NESY 2023, 2023,
  • [2] Semantic Probabilistic Layers for Neuro-Symbolic Learning Kareem
    Ahmed, Kareem
    Teso, Stefano
    Chang, Kai-Wei
    Van den Broeck, Guy
    Vergari, Antonio
    NEURAL-SYMBOLIC LEARNING AND REASONING 2023, NESY 2023, 2023,
  • [3] One Possibility of a Neuro-Symbolic Integration
    Samsonovich, Alexei, V
    BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES 2021, 2022, 1032 : 428 - 437
  • [4] Neuro-symbolic approaches in artificial intelligence
    Hitzler, Pascal
    Eberhart, Aaron
    Ebrahimi, Monireh
    Sarker, Md Kamruzzaman
    Zhou, Lu
    NATIONAL SCIENCE REVIEW, 2022, 9 (06)
  • [5] Overcoming Recommendation Limitations with Neuro-Symbolic Integration
    Carraro, Tommaso
    PROCEEDINGS OF THE 17TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, RECSYS 2023, 2023, : 1325 - 1331
  • [6] Neuro-Symbolic Constraint Programming for Structured Prediction
    Dragone, Paolo
    Teso, Stefano
    Passerini, Andrea
    NESY 2021: NEURAL-SYMBOLIC LEARNING AND REASONING, 2021, 2986 : 6 - 14
  • [7] ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI
    Shumsky, Sergey
    Baskov, Oleg
    ARTIFICIAL GENERAL INTELLIGENCE, AGI 2023, 2023, 13921 : 255 - 264
  • [8] Towards Neuro-Symbolic Reinforcement Learning For Trustworthy Human-Autonomy Teaming
    Gurung, Priti
    Li, Jiang
    Rawat, Danda B.
    ASSURANCE AND SECURITY FOR AI-ENABLED SYSTEMS, 2024, 13054
  • [9] ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
    Jain, Monika
    Singh, Kuldeep
    Mutharaju, Raghava
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: RESEARCH TRACK, ECML PKDD 2023, PT IV, 2023, 14172 : 230 - 247
  • [10] Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer
    Carraro, Tommaso
    Daniele, Alessandro
    Aiolli, Fabio
    Serafini, Luciano
    ADVANCES IN INFORMATION RETRIEVAL, ECIR 2024, PT III, 2024, 14610 : 226 - 242