Agents & Multi-Agent Systems

Learning Outcomes:

• Defining the key characteristics and concepts of intelligent agents
• Analyzing different agent architectures and their capabilities
• Designing autonomous agents for specific environments and tasks
• Implementing agent communication and coordination mechanisms
• Evaluating multi-agent systems and their collective behaviors
• Applying game theory concepts to agent decision-making
• Developing strategies for negotiation between agents
• Constructing voting protocols for group decision-making
• Formulating auction mechanisms for resource allocation
• Reasoning with arguments in agent dialogues and debates
• Comparing different approaches to agent planning and learning

Skills for module:

Intelligent Agents

Artificial Intelligence

Machine Learning

Reinforcement Learning

Logics

Problem Solving

Algorithms

Data Structures

Probability

Statistics

Python

Neural Networks

Calculus

Discrete

Critical Thinking

Time Management