Collective Problem Solving with Multi-Agent Systems Training is an advanced program exploring how multiple autonomous artificial intelligence agents can interact within a shared environment to address complex problems.

In Multi-Agent Systems (MAS), agents with different roles and capabilities can communicate, exchange information, negotiate, and cooperate to achieve shared objectives. Depending on the environment, they may also compete to pursue individual goals.

The course begins with the fundamental characteristics of Intelligent Agents and progresses through BDI (Belief–Desire–Intention) architectures, Game Theory, Distributed Problem Solving, and mechanisms governing interactions between agents.

Participants examine communication protocols such as FIPA-ACL and KQML, alongside coordination approaches including Auctions, Contract Net, Stigmergy, Negotiation, and Coalition Formation.

The program also introduces Multi-Agent Reinforcement Learning (MARL) algorithms such as Independent Q-Learning, CTDE, QMIX, and MADDPG, simulations using PettingZoo, and LLM-powered generative agent systems developed with frameworks such as AutoGen and LangChain.

The training aims to equip participants with the knowledge needed to design adaptive and resilient distributed AI solutions for applications including supply chain optimization, logistics networks, financial market simulations, and robotic systems.

For corporate groups, we can organize this training as an on-site private group.

Prerequisites

There are no mandatory prerequisites for attending this training.

Who Should Attend

Robotics Engineers working on Swarm Robotics and multi-robot coordination
Simulation Specialists modeling economic markets, social systems, and supply chains
Quantitative Analysts
Blockchain and Systems Architects developing solutions for DAO and Web3 ecosystems
Game AI Developers creating dynamic virtual environments
Senior AI Engineers developing complex and distributed artificial intelligence systems
AI Researchers

What You Will Learn

Upon completing this training, you will be able to:

Explain the characteristics of Intelligent Agents and identify different agent classes.
Model agent behavior using BDI architectures and Game Theory.
Understand and apply communication protocols such as FIPA-ACL and KQML.
Evaluate coordination mechanisms including Auctions, Contract Net, and Stigmergy.
Develop Negotiation and Coalition Formation strategies.
Apply MARL algorithms such as Independent Q-Learning, CTDE, QMIX, and MADDPG.
Conduct multi-agent simulations using PettingZoo.
Design LLM-powered generative agent systems with AutoGen and LangChain.
Develop an end-to-end project based on a decentralized logistics network scenario.
Evaluate the ethical, security, and research considerations associated with Multi-Agent Systems.

Training Outline

1. Philosophy and Theoretical Foundations of Multi-Agent Systems

Agent Concepts and Fundamental Characteristics

Understanding Intelligent Agents
Fundamental agent characteristics:
- Autonomy
- Reactivity
- Proactivity
- Social Ability
Evolution from Simple Reflex Agents to reasoning-based Deliberative Agents
Classification of agents
Exploring different Environment types

Agent Theory and Cognitive Architectures

Fundamental concepts of Agent Theory
BDI (Belief–Desire–Intention) architecture
Modeling agent behavior through beliefs, goals, and intentions

Game Theory

Analyzing strategic interactions between agents using Game Theory
Normal Form Games
Extensive Form Games
Nash Equilibrium
Prisoner's Dilemma
Evaluating cooperation and competition between agents

Distributed Problem Solving

Understanding Distributed Problem Solving
Breaking complex tasks into smaller subtasks
Combining results produced by different agents
Collective problem-solving processes

2. Agent Architectures and Communication Protocols

This section explores agent architectures and the communication approaches that enable information exchange between autonomous agents.

Examining Agent Architectures
Architectural considerations for BDI and other agent behavior models
Inter-agent communication
FIPA-ACL (Foundation for Intelligent Physical Agents – Agent Communication Language)
KQML (Knowledge Query and Manipulation Language)
Messaging and information sharing in multi-agent environments

3. Coordination, Negotiation, and Organizational Mechanisms

Coordination between autonomous agents
Auction-based task and resource allocation
Contract Net mechanisms
Indirect coordination through Stigmergy
Negotiation strategies
Coalition Formation
Cooperative and competitive organizational structures

4. Multi-Agent Reinforcement Learning (MARL)

This section focuses on Multi-Agent Reinforcement Learning (MARL) methods that enable multiple agents to learn within cooperative or competitive environments.

Introduction to Multi-Agent Reinforcement Learning
Independent Q-Learning
CTDE (Centralized Training, Decentralized Execution)
QMIX
MADDPG (Multi-Agent Deep Deterministic Policy Gradient)
The impact of agent interactions on learning processes
Hands-on multi-agent simulations using PettingZoo

5. LLM-Based Agent Simulations and Generative Agents

Agent systems powered by Large Language Models (LLMs)
Generative Agents
Developing multi-agent structures with AutoGen
Agent-based applications using LangChain
Cooperation and task execution in LLM-based agent systems
Evaluating multi-agent simulations

6. Practical Project and System Evaluation

Practical and evaluation topics identified in the source learning outcomes:

Decentralized logistics network simulation
Collaborative task execution involving multiple agents
Developing an end-to-end multi-agent project
Ethical and security considerations in agent systems
Future research directions in multi-agent systems

Why Choose Us

Leading UK-based global training provider since 1995.

Experience Collective Problem Solving with Multi-Agent Systems in Canada through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

Delivery Method: Online Instructor-Led
Participation Model: Public / Private (In-house)
Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
Worldwide Access: Join our live virtual classrooms from Canada or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience Collective Problem Solving with Multi-Agent Systems through face-to-face Classroom Based training in Canada. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

Delivery Method: Classroom Based
Participation Model: Public / Private (In-house)
Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Collective Problem Solving with Multi-Agent Systems in Canada as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

Delivery Method: Onsite
Participation Model: Private (In-house)
Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
Worldwide Onsite Delivery: Arrange the training in Canada or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.

Collective Problem Solving with Multi-Agent Systems Training in Canada