Knowledge refinement mechanism in agency using adaptive automata and genetic algorithms
Recent advancements in data mining techniques showcase expansive and effective insights. Yet, these insights often remain incomplete. In situations demanding optimal results, such incomplete knowledge from various agents falls short. This paper delves into the role of mobile agents engaging in data mining within a multi-agent environment. Each agent is tailored with distinct goals, mining data in line with its identified problem set. Mobile agents understand insights from relevant agents equipped with specialized data knowledge. Outcomes from diverse agents converge, serving as foundational data for mobile agents. This integration is facilitated by integrating adaptive automata and genetic algorithms, enhancing the mobile agent’s expertise on a particular task.
Authors
- Liaqat Ali Waseem (ORCID: https://orcid.org/0000-0003-3971-0758)
- M. Hameed (ORCID: https://orcid.org/0000-0002-2838-2106)
- Wasim Ahmad Khan (ORCID: https://orcid.org/0000-0002-6499-8100)
- Arslan Ahmad Siddiqi (ORCID: https://orcid.org/0000-0002-2430-1145)
- Muhammad Ayub (ORCID: https://orcid.org/0000-0002-7111-1571)
- Muhammad Umer Quddoos (ORCID: https://orcid.org/0000-0002-1498-6822)
- Mahmud Khan (ORCID: https://orcid.org/0000-0001-6248-1194)
- Muhammad Adeel (ORCID: https://orcid.org/0009-0005-9539-6654)
- Muhammad Abdul Basit Ur Rahim (ORCID: https://orcid.org/0000-0003-4914-6839)
Institutions
- California State University System (US)
- Bahauddin Zakariya University (PK)
- COMSATS University Islamabad (PK)
- Government College University, Faisalabad (PK)
- Industrial Systems and Control (United Kingdom) (GB)
- Baba Guru Nanak University (PK)
Publication Details
- Journal
- Journal of Infrastructure Policy and Development
- Published
- 2024-12-28
- DOI
- https://doi.org/10.24294/jipd9482
- Primary Topic
- Reinforcement Learning in Robotics
- Type
- article
- Field-Weighted Citation Impact
- 0.00