Lectures related to Agent Applications: Autonomous UAVs and MAS Interactions

Intelligent Agents: Characteristics and Applications

Covers the characteristics, applications, and challenges of intelligent agents in software systems, emphasizing their role in making autonomous decisions and coordinating with other agents.

Multiagent Systems: Planning and Coordination

Explores the use of multi-agent systems for planning, coordination, and task assignment among heterogeneous agents.

Agent Platforms: Types, Intelligence, and Communication

Explores agent platforms, intelligence, and communication models in multiagent systems.

Consensusability of linear interconnected multi-agent systems

Explores the consensusability of linear interconnected multi-agent systems, focusing on achieving consensus in multi-agent systems.

Game Theory Applications: Project Proposals and Examples

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

Introduction to Game Theory

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.

Agent Technology in Manufacturing: DaimlerChrysler Case Study

Explores DaimlerChrysler's use of agent technology in manufacturing, focusing on the Production 2000+ system and its benefits.

Safe Learning for Autonomous Systems

Explores challenges in control, safety, and coordination for autonomous systems like autonomous cars, focusing on safe learning and Nash equilibria.

Markov Games: Concepts and Applications in Reinforcement Learning

Covers Markov games, their dynamics, equilibria, and applications in reinforcement learning.

Virtual Humans: Creation and Applications

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

Multi-Agent Systems: Traffic Management and Pricing Strategies

Covers multi-agent systems in traffic management and dynamic pricing strategies using game theory.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Equilibrium State Prices Determination

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

Static Games: Introduction to Game Theory Concepts

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

Intelligent Agents: Making Decisions and Planning

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

Reinforcement Learning Basics

Introduces the basics of reinforcement learning, including Q-learning and epsilon-greedy policies.

Reinforcement Learning for Pacman

Explores applying reinforcement learning to teach Pacman to play autonomously using policy gradient methods and Markov decision processes.

Zero-Sum Games: Concepts and Applications

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

Catastrophe Modeling: Storms

By Professor David Bresch explores the origins and applications of natural catastrophe modeling, focusing on storms and their impact on risk mitigation and decision-making processes.

Potential Games: Best Response Dynamics and Nash Equilibria

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