Intelligent Agents: Characteristics and Applications

Related lectures (30)

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Provides guidance on project proposals in game theory, showcasing past examples and emphasizing problem formulation and mathematical modeling.

Covers intelligent agents, decision-making, planning, machine learning, and game theory.

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Covers the application of reinforcement learning to teach Pacman to play autonomously by trial and error.

Covers the basics of static games and introduces key game theory concepts, including the Prisoner's Dilemma and Nash Equilibrium.

Covers zero-sum games, focusing on Nash equilibria, security strategies, and their applications in various scenarios.

Explains the determination of equilibrium state prices in asset pricing through consumption market clearing and budget constraints.

Delves into the creation of virtual humans, immersive VR systems, challenges in controlling digital facsimiles, and applications in medicine.

Covers potential games, best response dynamics, and the convergence to Nash equilibria.

Covers potential games, their properties, and the convergence of best-response dynamics to Nash equilibria.