This episode introduces Multi-Agent Systems (MAS), exploring how collections of autonomous agents interact, communicate, cooperate, and coordinate to solve complex problems beyond the scope of individual agents. Agent Communication Languages (ACLs), such as FIPA-ACL and KQML, based on Speech Act Theory, facilitate these interactions. The differences between centralized control systems, which rely on a single decision-making agent, and distributed control systems, where autonomous agents make local decisions, are examined. Emergent behavior, where complex global patterns arise from simple local interactions, and negotiation mechanisms like auctions and the Contract Net Protocol, are key concepts. Real-world applications of MAS, from intelligent traffic control systems to collaborative robots in warehouses, illustrate the practical significance of these systems. The study of MAS shifts focus from individual agents to the dynamics of a "digital society," with the choice between centralized and distributed contr

This discussion focuses on the core mechanisms of planning and goal-oriented behavior in agents, detailing how they formulate objectives and devise action sequences to achieve them. Goal formulation is enhanced by utility functions that assign numerical scores to states, enabling agents to search for optimal paths. Classical planning models, such as STRIPS, represent problems using states, actions, and goals, while Hierarchical Task Network (HTN) planning offers a more human-like, top-down approach. The Planning Domain Definition Language (PDDL) is introduced as a standard for describing planning problems, separating domain physics from specific problem instances. Robust agents must handle uncertainty through goal prioritization and replanning, adapting their strategies in response to changing environments or failed actions. Planning allows agents to reason about the future and formulate coherent strategies, with the true measure of an agent's intelligence being its resilience and adaptability in the face of

This segment examines the architectural blueprints that underpin the design of intelligent agents, dictating the flow of information from perception to planning to action. Central to an agent's operation is the Perception-Planning-Action loop, where the planning phase serves as the cognitive core, updating the world model, generating options, evaluating outcomes, and selecting actions. Deliberative agents, which rely on detailed internal models for comprehensive planning, are contrasted with reactive agents that map sensor inputs directly to actions using simple rules. Hybrid architectures, such as the Subsumption Architecture and the BDI (Beliefs, Desires, Intentions) Architecture, combine the strengths of both approaches, offering both goal-directed and responsive behaviors. An agent's autonomy level is closely tied to the sophistication of its internal environment model, as defined by the NIST hierarchy of autonomy. We also explore various state representations, from atomic to structured, and their influe