Studying Transaction Fees in the Bitcoin Blockchain with Probabilistic Logic Programming

被引:18
作者
Azzolini, Damiano [1 ]
Riguzzi, Fabrizio [2 ]
Lamma, Evelina [1 ]
机构
[1] Univ Ferrara, Dipartimento Ingn, Via Saragat 1, I-44122 Ferrara, Italy
[2] Univ Ferrara, Dipartimento Matemat & Informat, Via Saragat 1, I-44122 Ferrara, Italy
关键词
bitcoin; blockchain; probabilistic logic programming;
D O I
10.3390/info10110335
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In Bitcoin, if a miner is able to solve a computationally hard problem called proof of work, it will receive an amount of bitcoin as a reward which is the sum of the fees for the transactions included in a block plus an amount inversely proportional to the number of blocks discovered so far. At the moment of writing, the block reward is several orders of magnitude greater than the sum of transaction fees. Usually, miners try to collect the largest reward by including transactions associated with high fees. The main purpose of transaction fees is to prevent network spamming. However, they are also used to prioritize transactions. In order to use the minimum amount of fees, users usually have to find a compromise between fees and urgency of a transaction. In this paper, we develop a probabilistic logic model to experimentally analyze how fees affect confirmation time and miner's revenue and to predict if an increase of average fees will generate a situation when the miner gets more reward by not following the protocol.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] Probabilistic (logic) programming concepts
    De Raedt, Luc
    Kimmig, Angelika
    [J]. MACHINE LEARNING, 2015, 100 (01) : 5 - 47
  • [32] Probabilistic Logic Programming in Action
    Fadja, Arnaud Nguembang
    Riguzzi, Fabrizio
    [J]. TOWARDS INTEGRATIVE MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 2017, 10344 : 89 - 116
  • [33] Probabilistic (logic) programming concepts
    Luc De Raedt
    Angelika Kimmig
    [J]. Machine Learning, 2015, 100 : 5 - 47
  • [34] A history of Probabilistic Inductive Logic Programming
    Riguzzi, Fabrizio
    Bellodi, Elena
    Zese, Riccardo
    [J]. FRONTIERS IN ROBOTICS AND AI, 2014,
  • [35] Statistical Statements in Probabilistic Logic Programming
    Azzolini, Damiano
    Bellodi, Elena
    Riguzzi, Fabrizio
    [J]. LOGIC PROGRAMMING AND NONMONOTONIC REASONING, LPNMR 2022, 2022, 13416 : 43 - 55
  • [36] Neural probabilistic logic programming in DeepProbLog
    Manhaeve, Robin
    Dumancic, Sebastijan
    Kimmig, Angelika
    Demeester, Thomas
    De Raedt, Luc
    [J]. ARTIFICIAL INTELLIGENCE, 2021, 298
  • [37] COALGEBRAIC SEMANTICS FOR PROBABILISTIC LOGIC PROGRAMMING
    Gu, Tao
    Zanasi, Fabio
    [J]. LOGICAL METHODS IN COMPUTER SCIENCE, 2021, 17 (02) : 2:1 - 2:35
  • [38] Probabilistic Spatial Reasoning in Constraint Logic Programming
    Schultz, Carl
    Bhatt, Mehul
    Suchan, Jakob
    [J]. SCALABLE UNCERTAINTY MANAGEMENT, SUM 2016, 2016, 9858 : 289 - 302
  • [39] Agent-Oriented Probabilistic Logic Programming
    Jie Wang
    Shi-Er Ju
    Chun-Nian Liu
    [J]. Journal of Computer Science and Technology, 2006, 21 : 412 - 417
  • [40] On the implementation of the probabilistic logic programming language ProbLog
    Kimmig, Angelika
    Demoen, Bart
    De Raedt, Luc
    Costa, Vitor Santos
    Rocha, Ricardo
    [J]. THEORY AND PRACTICE OF LOGIC PROGRAMMING, 2011, 11 : 235 - 262