publications

2023

1. arXiv

   Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition

   Esmaeili, Ahmad, Ghorrati, Zahra, and Matson, Eric T.

   2023

   arXiv Bib

   @misc{esmaeili2023distributed,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition},
     author = {Esmaeili, Ahmad and Ghorrati, Zahra and Matson, Eric T.},
     year = {2023},
     eprint = {2311.04236},
     archiveprefix = {arXiv},
     primaryclass = {eess.SP},
     arxiv = {2311.04236},
     selected = {true}
   }

2. arXiv

   Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach

   Esmaeili, Ahmad, Rayz, Julia T., and Matson, Eric T.

   2023

   arXiv Bib

   @misc{esmaeili2023hybrid,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach},
     author = {Esmaeili, Ahmad and Rayz, Julia T. and Matson, Eric T.},
     year = {2023},
     eprint = {2309.06604},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     arxiv = {2309.06604},
     selected = {true}
   }

3. arXiv

   Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization

   Esmaeili, Ahmad, Ghorrati, Zahra, and Matson, Eric T.

   arXiv preprint 2023

   arXiv Bib

   @article{esmaeili2023agentbased,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization},
     author = {Esmaeili, Ahmad and Ghorrati, Zahra and Matson, Eric T.},
     journal = {arXiv preprint },
     year = {2023},
     arxiv = {2205.05272},
     selected = {true}
   }

2022

1. arXiv

   Hierarchical Collaborative Hyper-parameter Tuning

   Esmaeili, Ahmad, Ghorrati, Zahra, and Matson, Eric

   arXiv preprint 2022

   Abs arXiv Bib

   Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.

   @article{esmaeili2022hierarchical,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Hierarchical Collaborative Hyper-parameter Tuning},
     author = {Esmaeili, Ahmad and Ghorrati, Zahra and Matson, Eric},
     journal = {arXiv preprint },
     year = {2022},
     arxiv = {2205.05272},
     selected = {true},
     doi = {10.48550/ARXIV.2205.05272}
   }

2. TAAS

   HAMLET: A Hierarchical Agent-based Machine Learning Platform

   Esmaeili, A., Gallager, J. C., Springer, J. A., and Matson, E. T.

   ACM Transactions on Autonomous and Adaptive Systems, 2022

   Abs Bib Code

   Hierarchical Multi-agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this article, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research and democratization of geographically and/or locally distributed machine learning entities. The proposed system models machine learning solutions as a hypergraph and autonomously sets up a multi-level structure of heterogeneous agents based on their innate capabilities and learned skills. HAMLET aids the design and management of machine learning systems and provides analytical capabilities for research communities to assess the existing and/or new algorithms/datasets through flexible and customizable queries. The proposed hybrid machine learning platform does not assume restrictions on the type of learning algorithms/datasets and is theoretically proven to be sound and complete with polynomial computational requirements. Additionally, it is examined empirically on 120 training and 4 generalized batch testing tasks performed on 24 machine learning algorithms and 9 standard datasets. The provided experimental results not only establish confidence in the platform’s consistency and correctness but also demonstrate its testing and analytical capacity

   @article{esmaeili2022hamlet,
     abbr = {TAAS},
     bibtex_show = {true},
     title = {HAMLET: A Hierarchical Agent-based Machine Learning Platform},
     author = {Esmaeili, A. and Gallager, J. C. and Springer, J. A. and Matson, E. T.},
     journal = {ACM Transactions on Autonomous and Adaptive Systems, },
     year = {2022},
     publisher = {ACM},
     code = {https://github.com/aesmaeili/HAMLET},
     selected = {true}
   }

2019

1. PAAMS

   Towards Topological Analysis of Networked Holonic Multi-agent Systems

   Esmaeili, A., Mozayani, N., Jahed-Motlagh, M. R., and Matson, E. T.

   In International Conference on Practical Applications of Agents and Multi-Agent Systems, 2019

   Abs Bib

   Interaction networks, either being implicitly or explicitly specified between the agents, play a crucial role in all multi-agent systems. These structures define and limit the ways the agents should interact with their peers, and hence help to manage the coordination problems in large-scale systems. It is widely accepted that the structure of an interaction network plays a significant role in the performance of the systems. In this article, we use the interaction network of the initial agent population to construct a holonic multi-agent system. Being based on agent interaction networks, the performance of the resulting holonic multi-agent system highly depends on the structure of the underlying agent network. Here, we study this dependency in more details. The study is carried out by applying the holonification algorithm on various network topologies and assessing the constructed holonic structure in a task environment.

   @inproceedings{esmaeili2019towards,
     abbr = {PAAMS},
     bibtex_show = {true},
     author = {Esmaeili, A. and Mozayani, N. and Jahed-Motlagh, M. R. and Matson, E. T.},
     title = {Towards Topological Analysis of Networked Holonic Multi-agent Systems},
     booktitle = {International Conference on Practical Applications of Agents and Multi-Agent Systems, },
     year = {2019},
     publisher = {Springer International Publishing},
     address = {Cham},
     pages = {42--54}
   }

2018

1. IRC

   Multi-agent Cooperation Using Snow-Drift Evolutionary Game Model: Case Study in Foraging Task

   Esmaeili, A., Ghorrati, Z., and Matson, E. T.

   In IEEE International Conference on Robotic Computing, 2018

   Abs Bib

   Cooperation is often considered as one of the key and unclear concepts, which differentiates multi-agent systems from other related fields such as distributed computing. One of the popular benchmarks for the verification of the effectiveness of various cooperation algorithms is multi-agent foraging task. Different approaches have been proposed among which Markov game based ones are widely used, though they could not select consistent equilibrium for the group. In this paper, an evolutionary game based method is proposed. In this method, the interactions among the agents are modeled by snow-drift game to evolve the evolutionary stable strategy (ESS) and bring the maximal reward for the group of agents. The simulation verified the efficiency of the proposed algorithm.

   @inproceedings{esmaeili2018multi,
     abbr = {IRC},
     bibtex_show = {true},
     title = {Multi-agent Cooperation Using Snow-Drift Evolutionary Game Model: Case Study in Foraging Task},
     author = {Esmaeili, A. and Ghorrati, Z. and Matson, E. T.},
     booktitle = {IEEE International Conference on Robotic Computing, },
     pages = {308--312},
     year = {2018},
     organization = {IEEE}
   }

2017

1. CSR

   A Socially-based Distributed Self-organizing Algorithm for Holonic Multi-agent Systems: Case Study in a Task Environment

   Esmaeili, A., Mozayani, N., Jahed-Motlagh, M. R., and Matson, E. T.

   Cognitive Systems Research, 2017

   Abs Bib

   Holonic multi-agent systems (HMASs) have recently attracted many researches in multi-agent systems community. Inspired from the multi-level and self-similar structures of social and biological system, holonic multi-agent systems have been widely used to model and solve complex real-world problems. The main concern in deploying HMASs is the problem of building the hierarchical holonic structure, called holarchy, and dynamically managing it during its lifetime. The way an HMAS is organized has a great impact on its applicability and performance. This paper proposes a self-organizing algorithm to build and manage the holoic structures in multi-agent systems. This algorithm is based on the local information of the agents about other agents they can communicate with. Using common social concepts, like skills, diversity, social exchange theory, and norms in definition of the algorithm, the outcomes of this research can be used in wide ranges of distributed applications. The proposed model is extensively tested in a task allocation problem; and its performance based on various design parameters is studied. Empirical results show that the proposed model properly increases the performance of the system in terms of effectiveness and efficiency.

   @article{esmaeili2017social,
     abbr = {CSR},
     bibtex_show = {true},
     title = {A Socially-based Distributed Self-organizing Algorithm for Holonic Multi-agent Systems: Case Study in a Task Environment },
     author = {Esmaeili, A. and Mozayani, N. and Jahed-Motlagh, M. R. and Matson, E. T.},
     journal = {Cognitive Systems Research, },
     volume = {43},
     pages = {21 - 44},
     year = {2017},
     issn = {1389-0417}
   }

2016

1. EAAI

   The Impact of Diversity on Performance of Holonic Multi-agent Systems

   Esmaeili, A., Mozayani, N., Jahed-Motlagh, M. R., and Matson, E. T.

   Engineering Applications of Artificial Intelligence, 2016

   Abs Bib

   There are Numerous researches in the fields of social sciences and multi-agent systems that are dedicated to studying the role of diversity vs. individual capabilities in design and performance of human and artificial societies. This paper addresses more complex multi-level structures, namely holarchies, in multi-agent systems, and conducts several analyses on the effect of individuals’ behavioral diversities in the constitution and performance of the whole system. The hypothesis is that diversity directly affects the structure and performance of a holonic organization. In order to check this, we first proposed a diversity-based holonic model for multi-agent systems and then carried out a set of simulations in which a holonic multi-agent system is employed in a function optimization application. In these simulations, we manipulated the behavioral diversity of the agents in various ways and carefully observed the changes that occur on the effectiveness of the system. These observations include the quality of solutions, the height and composition of the constructed systems, and the time efficiency of the model. The empirical results show that as the diversity of intra-holon agents increases, the height of holarchy is reduced while the sizes of the holons tend to increase. On the other hand, this increase in diversity results in an increase in the performance of the system in situations that diversity of the initial population is high. However, when the diversity of the initial population is low, the diversities inside the holons need to be adjusted around 60 percent in order to boost the performance.

   @article{esmaeili2016impact,
     abbr = {EAAI},
     bibtex_show = {true},
     title = {The Impact of Diversity on Performance of Holonic Multi-agent Systems },
     author = {Esmaeili, A. and Mozayani, N. and Jahed-Motlagh, M. R. and Matson, E. T.},
     journal = {Engineering Applications of Artificial Intelligence, },
     volume = {5},
     pages = {186 - 201},
     year = {2016},
     issn = {0952-1976}
   }

2015

1. RITA

   Golf Playing DARwIn-OP: A theoretical approach

   Gomez, M., Khaday, B., Gonzalez, A., Esmaeili, A., and Matson, E. T.

   In The International Conference on Robot Intelligence Technology and Applications, 2015

   Bib

   @inproceedings{gomez2015golf,
     abbr = {RITA},
     bibtex_show = {true},
     author = {Gomez, M. and Khaday, B. and Gonzalez, A. and Esmaeili, A. and Matson, E. T.},
     booktitle = {The International Conference on Robot Intelligence Technology and Applications, },
     title = {Golf Playing DARwIn-OP: A theoretical approach},
     year = {2015},
     month = dec,
     address = {Bucheon, South Korea}
   }

2. IJEC

   A Goal-based Holonification Approach for Holonic Multi-agent Systems

   Esmaeili, A., Mozayani, N., and Jahed-Motlagh, M. R.

   Iranian Journal of Electrical and Computer Engineering, 2015

   Bib

   @article{esmaeili2015goal,
     abbr = {IJEC},
     bibtex_show = {true},
     author = {Esmaeili, A. and Mozayani, N. and Jahed-Motlagh, M. R.},
     title = {A Goal-based Holonification Approach for Holonic Multi-agent Systems},
     journal = {Iranian Journal of Electrical and Computer Engineering, },
     year = {2015}
   }

2014

1. IST

   An EDA-based community detection in complex networks

   Ghasemi Parsa, M., Mozayani, N., and Esmaeili, A.

   In International Symposium on Telecommunications, 2014

   Bib

   @inproceedings{parsa2014ead,
     abbr = {IST},
     bibtex_show = {true},
     title = {An EDA-based community detection in complex networks},
     author = {Ghasemi Parsa, M. and Mozayani, N. and Esmaeili, A.},
     booktitle = {International Symposium on Telecommunications, },
     year = {2014},
     pages = {476-480},
     month = sep
   }

2. ICIS

   Multi-level holonification of multi-agent networks

   Esmaeili, A., Mozayani, N., and Jahed-Motlagh, M. R.

   In IEEE International Conference on Intelligent Systems, 2014

   Bib

   @inproceedings{esmaeili2014multi,
     abbr = {ICIS},
     bibtex_show = {true},
     author = {Esmaeili, A. and Mozayani, N. and Jahed-Motlagh, M. R.},
     booktitle = {IEEE International Conference on Intelligent Systems, },
     title = {Multi-level holonification of multi-agent networks},
     year = {2014},
     month = feb,
     pages = {1-5},
     address = {Bam, Iran}
   }

2012

1. AMASTA

   Holonification of a Network of Agents Based on Graph Theory

   Abdoos, M., Esmaeili, A., and Mozayani, N.

   In Agent and Multi-Agent Systems. Technologies and Applications, 2012

   Bib

   @inproceedings{abdoos2012holonification,
     abbr = {AMASTA},
     bibtex_show = {true},
     booktitle = {Agent and Multi-Agent Systems. Technologies and Applications, },
     series = {Lecture Notes in Computer Science},
     title = {Holonification of a Network of Agents Based on Graph Theory},
     publisher = {Springer Berlin Heidelberg},
     author = {Abdoos, M. and Esmaeili, A. and Mozayani, N.},
     pages = {379-388},
     year = {2012},
     isbn = {978-3-642-30946-5}
   }

2. A Novel Association Rule Mining Method based on Multi-objective PSO Algorithm

   Nasiri, M., Esmaeili, A., Minaei, B., and Mozayani, N.

   Journal of Operational Research and Its Applications 2012

   Bib

   @article{nasiri2012novel,
     bibtex_show = {true},
     title = {A Novel Association Rule Mining Method based on Multi-objective PSO Algorithm},
     journal = {Journal of Operational Research and Its Applications},
     volume = {8},
     number = {4},
     year = {2012},
     issn = {2251-7286},
     author = {Nasiri, M. and Esmaeili, A. and Minaei, B. and Mozayani, N.}
   }

2010

1. AMASTA

   Improving Multi-agent Negotiations Using Multi-Objective PSO Algorithm

   Esmaeili, A., and Mozayani, N.

   In Agent and Multi-Agent Systems: Technologies and Applications, 2010

   Bib

   @inproceedings{esmaeili2010improving,
     abbr = {AMASTA},
     bibtex_show = {true},
     booktitle = {Agent and Multi-Agent Systems: Technologies and Applications, },
     series = {Lecture Notes in Computer Science},
     title = {Improving Multi-agent Negotiations Using Multi-Objective PSO Algorithm},
     publisher = {Springer Berlin Heidelberg},
     author = {Esmaeili, A. and Mozayani, N.},
     pages = {92-101},
     year = {2010},
     isbn = {978-3-642-13479-1}
   }

2. IJATS

   HoloJade: A Role Based Holonic Extension for JADE

   Esmaeili, A., and Mozayani, N.

   International Journal of Agent Technologies and Systems, 2010

   Bib

   @article{esmaeili2010holojade,
     abbr = {IJATS},
     bibtex_show = {true},
     author = {Esmaeili, A. and Mozayani, N.},
     title = {HoloJade: A Role Based Holonic Extension for JADE},
     journal = {International Journal of Agent Technologies and Systems, },
     publisher = {IGI Global},
     pages = {49-63},
     year = {2010}
   }

2009

1. ICEEE

   Proposing a new method for association rule mining using PSO algorithm

   Esmaeili, A., Nasiri, M., Minaei, B., and Mozayani, N.

   In IEEE International Conference on Electric and Electronic Engineering, 2009

   Bib

   @inproceedings{esmaeili2009proposing,
     abbr = {ICEEE},
     bibtex_show = {true},
     author = {Esmaeili, A. and Nasiri, M. and Minaei, B. and Mozayani, N.},
     booktitle = {IEEE International Conference on Electric and Electronic Engineering, },
     title = {Proposing a new method for association rule mining using PSO algorithm},
     year = {2009},
     month = apr
   }

2. CIMSA

   Adjusting the parameters of radial basis function networks using Particle Swarm Optimization

   Esmaeili, Ahmad, and Mozayani, Nasser

   In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2009

   Bib

   @inproceedings{esmaeili2009adjusting,
     abbr = {CIMSA},
     bibtex_show = {true},
     author = {Esmaeili, Ahmad and Mozayani, Nasser},
     booktitle = {IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, },
     title = {Adjusting the parameters of radial basis function networks using Particle Swarm Optimization},
     year = {2009},
     month = may,
     pages = {179 -181},
     issn = {}
   }