Job Title:
Freelancer Researcher – Multi-Agent Systems
Job Purpose:
The purpose of this role is to support the design, development, implementation, and evaluation of a novel Agent-Oriented Planning (AOP) framework for scalable, conflict-resilient, and adaptive decision-making in Multi-Agent Systems (MAS). The researcher will contribute to developing dynamic task allocation mechanisms, coalition-based game-theoretic conflict resolution models, hierarchical planning structures, and hybrid uncertainty management approaches. The role also includes simulation-based validation, performance benchmarking, and research documentation to support academic publication.
Required Qualification:
Master’s degree or PhD (completed or pursuing) in Computer Science, Artificial Intelligence, Data Science, Distributed Systems, Intelligent Systems / Machine Learning
Strong academic or applied background in Multi-Agent Systems (MAS) and Agent-Oriented Planning
Tools to be Familiar:
Programming & Modelling: Python (NumPy, Pandas, Matplotlib, SciPy), Reinforcement Learning libraries (e.g., Stable-Baselines, RLlib – preferred), Bayesian modelling libraries (e.g., PyMC, pgmpy) and MDP modelling frameworks.
Simulation Platforms (at least one): MATSim, SUMO, AnyLogic
Optimization & Game Theory: OR-Tools / PuLP / other optimization libraries, Game-theoretic modelling approaches and Nash equilibrium and bargaining solution modelling
Distributed Systems / Ledger Technologies: Basic understanding of Blockchain / Distributed Ledger Technology (DLT) concepts.
Required Experience
· Prior experience in: Multi-Agent Systems research, AI planning or distributed decision-making, Reinforcement learning or probabilistic modelling
· Experience conducting simulation-based experiments
· Experience with analytical modelling and performance evaluation
· Familiarity with Monte Carlo simulations and sensitivity analysis
· Prior academic publication (conference/journal) is an advantage
Required Knowledge/Skills
Strong understanding of:
- Agent-Oriented Planning principles
- Dynamic task allocation mechanisms
- Hierarchical planning models (e.g., Hierarchical MDPs)
- Coalition formation and game-theoretic conflict resolution
- Bayesian Networks and Markov Decision Processes
- Decision-making under uncertainty
- Scalability challenges in large-scale MAS
Ability to:
- Translate research objectives into implementable algorithms
- Design adaptive task prioritization mechanisms
- Implement reinforcement learning–based decision models
- Develop coalition-based negotiation strategies
- Integrate uncertainty modelling into planning systems
- Conduct simulation-driven validation
- Analyze performance metrics and compare baseline models
- Document methodologies clearly for academic publication
Strong analytical, mathematical, and problem-solving skills are essential
Job Description
The freelance researcher will be responsible for the following tasks:
1. Literature Review & Conceptual Framework Support
- Review recent literature on agent-oriented planning, MAS scalability, and decentralized coordination
- Assist in refining architectural components of the proposed AOP framework
- Identify suitable baseline models (e.g., Contract Net Protocol, hierarchical negotiation techniques) for comparison
2. Adaptive Task Prioritization & Workload Modelling
- Develop dynamic task prioritization mechanisms using reinforcement learning and predictive analytics
- Implement priority scoring functions incorporating urgency, dependency, and environmental dynamics
- Design flexible task reassignment logic during execution
3. Hierarchical Planning Model Development
- Assist in implementing a multi-layer hierarchical planning structure (Strategic–Tactical–Operational levels)
- Model decision processes using Hierarchical Markov Decision Processes (HMDPs)
- Evaluate planning efficiency across layered decision structures
4. Coalition-Based Conflict Resolution
- Design and implement coalition formation mechanisms among agents
- Model negotiation processes using game-theoretic approaches
- Implement Nash equilibrium and bargaining-based optimization strategies
- Evaluate conflict mitigation performance under varying agent densities
5. Uncertainty Management Integration
- Implement hybrid uncertainty modelling combining:
- Bayesian Networks
- Markov Decision Processes
- Confidence estimation techniques
- Conduct Monte Carlo simulations to assess decision reliability
- Perform sensitivity analysis under different uncertainty levels
6. DLT-Based Transparency & Audit Mechanism
- Support conceptual integration of Distributed Ledger Technology (DLT)
- Design mechanisms for recording task allocation and agent interactions
- Evaluate transparency and fault tolerance improvements
7. Simulation & Performance Evaluation
- Implement the proposed AOP framework in a simulation environment (e.g., traffic control or smart system scenario)
- Conduct empirical evaluation across multiple scenarios:
- High agent density
- Dynamic environmental shifts
- Time-sensitive coordination tasks
- Compare performance against existing MAS approaches
- Evaluate:
- Scalability
- Conflict reduction rate
- Decision latency
- Adaptability under uncertainty
- Resource utilization
8. Research Documentation & Publication Support
- Maintain reproducible, well-documented code
- Prepare experimental logs and structured datasets
- Generate figures, tables, and performance comparisons
- Assist in drafting methodology, results, and evaluation sections.
Contact : Gray 9566133822
Job Types: Part-time, Freelance, Volunteer
Contract length: 1 month
Pay: From ₹10,000.00 per month
Work Location: Remote
