Environment Types

• Fully/Partially Observable: Defines what information an agent can directly perceive from its environment.

• Deterministic vs Stochastic:

  • Deterministic: Future state of the environment is predictable.

  • Stochastic: Future state is influenced by random variables.

• Episodic vs Sequential:

  • Episodic: Decisions are made based on a single observation.

  • Sequential: Decisions depend on the previous observations.

• Static vs Dynamic (or Semi-Dynamic):

  • Static: The environment does not change when an agent is computing.

  • Dynamic: The environment may change during the computation.

• Discrete vs Continuous:

  • Discrete: Distinct and separate states.

  • Continuous: Infinite possible states, often represented by ranges.

• Single Agent vs Multi-Agent:

  • Single Agent: Only one agent acts in the environment.

  • Multi-Agent: Multiple agents interact within the same environment.

Key Characteristics of Multi-Agent Systems

• Collaboration: Agents work together to achieve common goals.

• Cooperation: Agents assist each other in achieving individual goals.

• Communication: Use of communication languages like KQML and FIPA-ACL.

• Competition: Agents strive to outperform others to achieve their goals.

Multi-Agent Learning Problems

• Agent Interaction: Each agent attempts to solve its learning problem while others solve theirs, leading to challenges such as:

  1. Non-stationarity in learning environments.

  2. Different scenarios: strategies must account for cooperation and self-interest.

• Information Gaps: Agents may lack knowledge about other agents' payoffs and learning strategies.

• Game Theory: Often used to provide solutions to interactions between agents, though it involves certain assumptions.

Classical vs. Modern Multi-Agent Systems

• Classical Systems: Focus on agents coordinating knowledge and activities to solve problems through distributed problem solving.

• Modern Systems: Incorporate advanced features such as machine learning, allowing agents to learn from experiences and adapt their strategies over time.

Characteristics of Multi-Agent Systems

• Incomplete Information: Each agent operates with partial knowledge.

• Decentralized Control: No single agent has control over the system, allowing flexibility.

• Asynchronous Computation: Agents operate independently, which can lead to non-coordinated outcomes.

Issues in Multi-Agent Systems

1. Task Decomposition: Determine how to break tasks into subtasks for agents.

2. Communication Protocols: Develop effective methods for agents to share information.

3. Managing Dependencies: Agents need to manage their interactions to avoid conflicts.

4. Conflict Resolution: Mechanisms for agents to address disagreements or inconsistencies in beliefs or goals.

5. Practical Engineering: Design multi-agent systems that are efficient and robust in real scenarios.

6. Balancing Computation vs Communication: Optimize resource usage to prevent bottlenecks.

7. Organizational Structures: Ability to create dynamic organizational frameworks.

8. Negotiation and Contracting: Facilitating agreement between agents to fulfill tasks.

9. Handling Inconsistency: Techniques to manage differing beliefs or goals among agents.

Cooperative Distributed Problem Solving (CDPS)

• Definition: CDPS examines how loosely coupled networks of problem solvers can achieve goals beyond individual capabilities through cooperation.

• Process: Involves problem decomposition, where tasks are divided, sub-problem solutions optimized, and results synthesized for final solutions.

Coordination Strategies

• Task-Sharing Protocol: Use of methods like Contract Net which includes stages:

  1. Recognition: Identify task needing assistance.

  2. Announcement: Broadcast task details and requirements.

  3. Bidding: Interested agents submit proposals based on their capabilities.

  4. Awarding: Select the best proposal.

  5. Expediting: Ensure the task is completed effectively.

Results Sharing**

• Methodologies: Agents share intermediate results during collaboration, enhancing overall efficiency and accuracy.

• Examples:

  • Blackboard Systems: Shared data structures where agents write partial solutions.

  • Subscribe/Notify Patterns: Enable proactive sharing of information upon relevant events.

Handling Inconsistency**

• Methods:

  1. Prevent inconsistencies from occurring initially.

  2. Resolve inconsistencies through negotiation and discussion.

  3. Build resilience into systems to manage inconsistencies gracefully.

Joint Intentions and Planning**

• Joint Intentions: Agents can have collective goals, known as joint persistent goals (JPGs), leading to collaborative task achievement.

• Planning: Utilizing multi-agent planning approaches that could be centralized or decentralized to manage actions of multiple agents effectively.

Conclusion**

• The lecture emphasized collaboration among agents, highlighting the importance of benevolence and cooperation in a multi-agent system. Strategies for coordination and problem-solving were critically analyzed, setting the foundation for future discussions on competitive scenarios in multi-agent systems.