Call for Papers
Special Issue Proposal - Emerging Advances in AI for Multi-Objective Decision Making in Multi-Agent Systems
SCOPE AND PURPOSE
In recent decades, there has been a sustainable increase in recognizing the necessity for autonomous and assisted decision-making techniques to go further than single-objective frameworks whenever confronted with complicated real-world problems that always entail multiple conflicting purposes. Additionally, it fosters cooperation and knowledge exchange among academics focusing on many fields of multi-objective decision-making and the themes listed underneath, and serves as a venue for the distribution of slightly elevated multi-objective decision-making investigation. Especially considering the premise that several real-world issue contexts are intrinsically multi-objective, the bulk of multi-agent technology systems try to enhance agents' strategies concerning a specific purpose. Multi-objective multi-agent technological-based analysis the trade-offs that may occur when competing for optimization techniques are considered. Additionally, their exceptions should be weighed regarding their benefit to the systems consumers. Researchers represent the consumer benefit by utilizing transfer functions that convert cost or yield arrays to numeric attributes, as is usual in multi-objective optimization.
This technique automatically develops distinct performance indicators: scalarised anticipated returns (SER) and expected scalarised returns (ESR). Researchers create a novel classification that categorizes multi-objective decision-making in multi-agent systems context according to their appropriate rewards and the kind and application of stochastic processes. This enables us to provide a systematic overview of the topic, explicitly define the present latest advance's multi-objective decision making in multi-agent systems, and highlight interesting opportunities for future learning. Researchers begin by analyzing whichever solution ideas applicable to the multiple configurations in the categorization, starting with the implementation stage, wherein the specified regulations are implemented, and the desired usefulness for users is accomplished.
Producing justifications that boost consumer happiness is extremely difficult, much as explaining choices in Multi-Agent Systems. Whenever the purpose of an Ai model is understood, individuals need clarification to comprehend and embrace the platform's judgments. The objective is especially true because when AI platform determinants of demand in multi-agent contexts, individuals don't understand the platform's aims, which may be contingent on the inclinations of many other inhabitants. In certain instances, interpretations would strive to enhance consumer happiness by considering the system's recommendation, user and other actors' desires, the ecosystem's characteristics, and qualities like justice, greed, and confidentiality.
Multi-agent platforms are indeed a central theme in modern artificial intelligence development. Multiple decision-making agents engage inside a connected, creating more sustainable similar or opposing objectives. Multi-agent systems techniques may be used for various applications, such as autonomous vehicles, multi-robot industries, computerized trade, commercial gaming, and computerized mentoring.
TOPICS INCLUDE, BUT ARE NOT LIMITED TO:
- Multi-objective Decision Making using advanced Reinforcement Learning
- Novel Game theory-based Multi-Attribute Decision Making
- Multi-Agent decision support systems using Evolutionary algorithms for autonomous agents
- Advanced Multi-objective planning and scheduling in Decision Making for Multi-Agent Systems
- AI-based algorithmic designs and architectures for Multi-Objective Decision Making in Multi-Agent Systems
- Semantic technologies, intelligent agents, and Expert systems for Multi-Objective Decision Making
- Advanced Machine Learning in Multi-Agent Systems
- Enhanced Bio-inspired algorithms for Multi-Objective Decision Making in Multi-Agent Systems
- AI-enabled Cognitive intelligence for Multi-Objective Decision Making
- Metaheuristic Optimisation for Multi-Objective Decision Making
TENTATIVE DATES
Submission Deadline: 25th June, 2025
Authors Notification: 05th September, 2025
Revised Version Submission: 10th December, 2025
Final Decision Notification: 12th February, 2026
GUEST EDITORS
Dr. Chi Lin
Senior member of IEEE, ACM, and CCF,
Associate Professor, Vice Advisor,
Institute of Intelligent System,
School of Software,
Dalian University of Technology,
Dalian, China.
Email: clindut@ieee.org
GS: https://scholar.google.com/citations?user=PVHo2-YAAAAJ
Dr. Chang Wu Yu
Professor,
Department of Computer Science and Information Engineering,
Chung Hua University,
Hsinchu, Taiwan.
Email: cwyu@chu.edu.tw
GS: https://scholar.google.com/citations?hl=zh-TW&user=M0nQiSwAAAAJ
Dr. Ning Wang
Assistant Professor,
Computer Science & Research,
Rowan University, Glassboro,
New Jersey, USA.
Email: wangn@rowan.edu
GS: https://scholar.google.com/citations?hl=zh-TW&user=OnrRV0AAAAAJ